Open-Source LLM Models
Practical, source-grounded guides to open-weight language models — parameters, licensing, hardware, fine-tuning, and when DEV.co can help you deploy or fine-tune them.
In this category
3b-de-ft-research_release
A 3.3B-parameter German language model fine-tuned from the Orpheus base model, released by Canopy Labs. Licensed under Apache 2.0 but gated (access restricted).…
A.X-K1
A.X K1 is a 519-billion-parameter sparse Mixture-of-Experts language model from SKT that activates only 33 billion parameters per token. It supports both reason…
acestep-5Hz-lm-4B
ACE-Step v1.5 is an open-source text-to-music generation model (4B parameters) designed to run on consumer hardware. It transforms text prompts into music in se…
alias-gpt2-small-x21
alias-gpt2-small-x21 is a GPT-2 variant developed by Stanford CRFM for text generation. It is open-source under Apache 2.0, publicly available, and compatible w…
Apertus-70B-Instruct-2509
Apertus-70B-Instruct-2509 is a 70-billion parameter open-source language model from Swiss AI designed for multilingual text generation. It supports over 1,800 l…
Apertus-8B-Instruct-2509
Apertus-8B-Instruct is an 8-billion-parameter open-weight language model trained on 15 trillion tokens with support for 1,811 languages. It is designed for mult…
Qwen2.5-0.5B-Instruct-GGUF
This is a quantized version of Qwen2.5-0.5B-Instruct optimized for local/offline inference using llama.cpp. The model is a 500M-parameter instruction-tuned lang…
Qwen2.5-1.5B-Instruct-GGUF
Qwen2.5-1.5B-Instruct-GGUF is a quantized version of Alibaba's 1.5-billion-parameter instruction-tuned language model, optimized for CPU and edge inference via …
Qwen2.5-7B-Instruct-GGUF
Qwen2.5-7B-Instruct-GGUF is a community-quantized version of Alibaba's 7B instruction-tuned language model, optimized for local inference via llama.cpp. It prov…
Qwen2.5-Coder-14B-Instruct-GGUF
Qwen2.5-Coder-14B-Instruct-GGUF is a quantized version of Alibaba's 14-billion-parameter code-focused language model, packaged in GGUF format for efficient loca…
Qwen2.5-Coder-32B-Instruct-GGUF
Qwen2.5-Coder-32B-Instruct-GGUF is a quantized version of Alibaba's 32-billion-parameter code-focused language model, optimized for local inference via llama.cp…
Qwen2.5-Coder-7B-Instruct-GGUF
Qwen2.5-Coder-7B-Instruct-GGUF is a quantized version of Alibaba's 7-billion-parameter code-focused language model, optimized for local inference via llama.cpp.…
bge-reranker-v2-gemma
bge-reranker-v2-gemma is a 2.5B-parameter text reranker model based on Google's Gemma-2B, designed to score the relevance of query-passage pairs. Unlike embeddi…
Bielik-11B-v3.0-Instruct
Bielik-11B-v3.0-Instruct is an 11B-parameter instruction-tuned LLM optimized for multilingual conversational tasks, supporting 20+ languages including Polish, E…
Bielik-11B-v3.0-Instruct-awq
Bielik-11B-v3.0-Instruct-awq is an 11-billion-parameter multilingual LLM optimized for Polish and 31 other European languages, distributed in AWQ (Activation-aw…
biogpt
BioGPT is a domain-specific generative language model developed by Microsoft, pre-trained on large-scale biomedical literature. It performs text generation and …
BioMistral-7B
BioMistral-7B is an open-source 7-billion-parameter language model built on Mistral, further trained on PubMed Central biomedical literature. It is designed for…
BMOJOF-primed-HQwen3-8B-Instruct
BMOJOF-primed-HQwen3-8B-Instruct is an 8B parameter instruction-tuned language model that replaces half its standard attention layers with B'MOJO-F hybrid layer…
codegen-350M-mono
CodeGen-350M-Mono is a 350-million-parameter autoregressive language model from Salesforce designed for program synthesis—generating executable code from Englis…
ctrl
CTRL is a 1.63B-parameter conditional transformer language model from Salesforce Research, trained on 140 GB of diverse text data (Wikipedia, Reddit, news, book…
Qwen3-Coder-Next-AWQ-4bit
Qwen3-Coder-Next is an open-weight, 80B-parameter language model optimized for coding tasks and agent deployment. Despite having only 3B activated parameters at…
DeepSeek-R1
DeepSeek-R1 is a 671B parameter mixture-of-experts reasoning model from DeepSeek AI, released under MIT license. It uses reinforcement learning and supervised f…
DeepSeek-R1-0528
DeepSeek-R1-0528 is a 685B parameter open-source reasoning model licensed under MIT. It demonstrates strong performance on math, code, and general reasoning ben…
DeepSeek-R1-0528-NVFP4-v2
This is NVIDIA's FP4-quantized version of DeepSeek R1-0528, a 393B-parameter transformer LLM optimized for inference efficiency. The model compresses weights an…
DeepSeek-R1-0528-Qwen3-8B
DeepSeek-R1-0528-Qwen3-8B is an 8.2B parameter open-source LLM distilled from DeepSeek-R1's reasoning capabilities into Qwen3's base model. It achieves strong p…
DeepSeek-R1-0528-Qwen3-8B-GGUF
DeepSeek-R1-0528-Qwen3-8B-GGUF is an 8-billion-parameter text generation model created by distilling chain-of-thought reasoning from DeepSeek-R1 into Qwen3's ba…
DeepSeek-R1-0528-Qwen3-8B-MLX-4bit
DeepSeek-R1-0528-Qwen3-8B-MLX-4bit is a 1.28B parameter, 4-bit quantized language model optimized for Apple Silicon devices. It is a community-maintained quanti…
DeepSeek-R1-0528-Qwen3-8B-MLX-8bit
DeepSeek-R1-0528-Qwen3-8B-MLX-8bit is a quantized 8-bit version of DeepSeek's 2.3B-parameter language model, optimized for Apple Silicon via MLX. It is a commun…
DeepSeek-R1-Distill-Llama-70B
DeepSeek-R1-Distill-Llama-70B is a 70B parameter language model distilled from DeepSeek-R1 (a reasoning-optimized model) and based on Llama-3.3-70B-Instruct. It…
DeepSeek-R1-Distill-Llama-8B
DeepSeek-R1-Distill-Llama-8B is an 8-billion-parameter language model distilled from the larger DeepSeek-R1 reasoning model. It is based on Meta's Llama-3.1-8B …
DeepSeek-R1-Distill-Qwen-1.5B
DeepSeek-R1-Distill-Qwen-1.5B is a 1.78B-parameter language model distilled from DeepSeek-R1, a larger reasoning-focused model. It is designed for text generati…
DeepSeek-R1-Distill-Qwen-1.5B-GGUF
DeepSeek-R1-Distill-Qwen-1.5B-GGUF is a 1.5 billion parameter distilled reasoning model from DeepSeek, quantized to GGUF format for local deployment. It is desi…
DeepSeek-R1-Distill-Qwen-32B
DeepSeek-R1-Distill-Qwen-32B is a 32.7B-parameter dense language model distilled from DeepSeek-R1, a large reasoning-focused model trained via reinforcement lea…
DeepSeek-R1-Distill-Qwen-7B
DeepSeek-R1-Distill-Qwen-7B is a 7.6B parameter language model distilled from DeepSeek-R1, a larger reasoning-focused model. It is trained on reasoning data fro…
DeepSeek-V3
DeepSeek-V3 is a 671-billion-parameter Mixture-of-Experts (MoE) language model that activates only 37B parameters per token, enabling efficient inference. This …
DeepSeek-V3-0324
DeepSeek-V3-0324 is a 684B-parameter open-source language model released by DeepSeek AI under the MIT license. It is an updated version of DeepSeek-V3 with repo…
DeepSeek-V3-0324-GGUF
DeepSeek-V3-0324-GGUF is a quantized version of DeepSeek's V3 model in GGUF format, optimized for local inference. It trades some accuracy for dramatically redu…
DeepSeek-V3-0324-NVFP4
NVIDIA's DeepSeek-V3-0324-NVFP4 is a quantized version of DeepSeek's large language model, optimized for production deployment. It reduces memory and compute re…
DeepSeek-V3.1
DeepSeek-V3.1 is a 671B-parameter hybrid LLM supporting both standard and 'thinking' modes, enabling reasoning-intensive tasks without requiring separate model …
DeepSeek-V3.2
DeepSeek-V3.2 is a 685B parameter open-source LLM released under MIT license. It emphasizes computational efficiency through sparse attention mechanisms and inc…
DeepSeek-V3.2-AWQ
DeepSeek-V3.2-AWQ is a 685B-parameter quantized language model from QuantTrio, based on DeepSeek-V3.2. It uses 4-bit AWQ quantization to reduce model size to ~3…
DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp is a 685B-parameter experimental LLM released under MIT license by deepseek-ai. It introduces DeepSeek Sparse Attention (DSA), a sparse attent…
DeepSeek-V4-Flash
DeepSeek-V4-Flash is a 284B-parameter mixture-of-experts language model with only 13B active parameters per inference, supporting 1-million-token context window…
DeepSeek-V4-Flash-DSpark
DeepSeek-V4-Flash-DSpark is a 284B-parameter Mixture-of-Experts language model with 13B active parameters, designed for efficient long-context inference up to 1…
DeepSeek-V4-Flash-GGUF
DeepSeek-V4-Flash-GGUF is an MIT-licensed, community-quantized version of DeepSeek's V4-Flash model in MXFP4 format, optimized for CPU and local GPU inference v…
DeepSeek-V4-Flash-NVFP4
DeepSeek-V4-Flash-NVFP4 is an MIT-licensed, NVIDIA-quantized version of DeepSeek AI's flagship 284B-parameter Mixture-of-Experts language model, reduced to 13B …
deepseek-v4-gguf
DeepSeek-V4-Flash GGUF is a quantized version of DeepSeek's V4 Flash model optimized for the ds4 inference engine. It uses aggressive quantization (2-bit and 4-…
DeepSeek-V4-Pro
DeepSeek-V4-Pro is a 1.6T-parameter Mixture-of-Experts model with 49B active parameters, supporting 1M-token context. Licensed under MIT, it is available ungate…
DeepSeek-V4-Pro-NVFP4
DeepSeek-V4-Pro-NVFP4 is a quantized Mixture-of-Experts language model with 1.6 trillion total parameters (49 billion active). NVIDIA has optimized it using the…
Devstral-Small-2505-4bit
Devstral-Small-2505-4bit is a 3.7B parameter language model converted to MLX (Apple Neural Engine) format and quantized to 4-bit precision. It is based on Mistr…
DialoGPT-medium
DialoGPT-medium is a GPT-2-based conversational AI model developed by Microsoft, trained on 147M multi-turn Reddit dialogues. It generates context-aware respons…
DialoGPT-small
DialoGPT-small is a 175M-parameter conversational AI model from Microsoft trained on 147M multi-turn Reddit discussions. It generates contextually relevant resp…
diffusiongemma-26B-A4B-it-NVFP4
DiffusionGemma-26B-A4B-IT-NVFP4 is a quantized, multimodal LLM from NVIDIA based on Google's Gemma 4 architecture. It processes text, image, and video inputs to…
distilgpt2
DistilGPT2 is a 82M-parameter English text-generation model created by Hugging Face as a compressed, faster version of GPT-2 (124M parameters) using knowledge d…
dolphin-2.9.1-yi-1.5-34b
Dolphin 2.9.1 Yi 1.5 34b is a 34B-parameter instruction-tuned LLM based on Yi-1.5-34B, fine-tuned on diverse conversational, coding, and agentic datasets. It re…
dots.mocr
dots.mocr is a 3B-parameter multilingual document parsing and image-to-text model licensed under MIT. It targets OCR, document layout analysis, table extraction…
dots.ocr
dots.ocr is a 3B-parameter vision-language model designed for multilingual document parsing, combining layout detection and text recognition in a single model. …
Dream-v0-Instruct-7B
Dream-v0-Instruct-7B is a 7.6B parameter open-source instruction-tuned language model released by Dream-org under the Apache 2.0 license. It is designed for tex…
EAGLE-LLaMA3-Instruct-8B
EAGLE is a speculative decoding framework that accelerates LLM inference by 2–5.6× without degrading output quality. It works by predicting future token probabi…
ERNIE-4.5-21B-A3B-PT
ERNIE-4.5-21B-A3B-PT is a 21-billion-parameter mixture-of-experts (MoE) language model from Baidu that activates only 3 billion parameters per token. It support…
Fanar-1-9B-Instruct
Fanar-1-9B-Instruct is a 9B-parameter instruction-tuned LLM developed by Qatar Computing Research Institute (QCRI) for Arabic-English bilingual conversational t…
fg-clip-base
FG-CLIP is a vision-language model that aligns images and text at fine-grained (patch and region) levels, not just global image-caption pairs. It uses a two-sta…
GDN-primed-HQwen3-8B-Instruct
GDN-primed-HQwen3-8B-Instruct is an 8B-parameter hybrid language model that mixes traditional attention layers with Gated DeltaNet (GDN) state-space model layer…
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF
Gemma-4-12B v2 is a 12B parameter LLM quantized to GGUF format, specialized for coding and agentic tool-use tasks. It runs locally on ~4.5 GB VRAM/unified memor…
gemma-4-12B-coder-fable5-composer2.5-v1
Gemma-4-12B-Coder is a fine-tuned 12-billion-parameter coding model built on Google's Gemma 4, specialized for Python algorithmic tasks. It uses verifiable trai…
gemma-4-12B-coder-fable5-composer2.5-v1-GGUF
Gemma-4-12B-Coder is a fine-tuned 12B parameter coding model optimized for local inference. It combines real chain-of-thought reasoning from passing test cases …
gemma-4-12b-heretic-abliterated-GGUF
A GGUF-quantized variant of Google's Gemma-4-12B model that has been modified ("abliterated") to remove refusal behaviors. Offered in multiple precision levels …
gemma-4-12B-it-assistant
Gemma 4 12B Unified is Google DeepMind's instruction-tuned, multimodal LLM with 11.95B parameters, supporting text, image, and audio input. It features a 256K t…
gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF
Gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF is a quantized, locally-runnable 12B parameter language model fine-tuned on Claude Opus reasoning data. It fits in 4.5–2…
gemma-4-12B-it-qat-q4_0-unquantized-assistant
Gemma 4 12B Unified is Google DeepMind's open-weight instruction-tuned LLM optimized with quantization-aware training (QAT). This variant is the unquantized Q4_…
gemma-4-26B-A4B-it-assistant
Gemma 4 26B A4B is Google DeepMind's open-source instruction-tuned multimodal LLM with ~26B total parameters but only ~3.8B active during inference (MoE archite…
Gemma-4-26B-A4B-it-NVFP4
Gemma-4-26B-A4B-it-NVFP4 is a community-quantized variant of Google's Gemma 4 Mixture-of-Experts model. It compresses the original 49 GB model to 16.5 GB via W4…
gemma-4-26B-A4B-it-uncensored
gemma-4-26B-A4B-it-uncensored is a 26B-parameter derivative of Google's Gemma 4 model with refusal behaviors systematically removed via abliteration. The model …
Gemma-4-26B-A4B-NVFP4
Gemma-4-26B-A4B-NVFP4 is NVIDIA's quantized version of Google's Gemma 4 multimodal LLM. It uses a mixture-of-experts architecture with 3.8B active parameters ou…
gemma-4-31B-it-assistant
Gemma 4 31B is Google DeepMind's instruction-tuned dense language model with 30.7 billion parameters. It supports text and image inputs, handles up to 256K toke…
gemma-4-31B-it-NVFP4-turbo
Gemma 4 31B IT NVFP4 Turbo is a quantized variant of Google's Gemma 4 31B instruction-tuned model, optimized for NVIDIA Blackwell GPUs (RTX 5090, RTX PRO 6000, …
gemma-4-31B-it-qat-q4_0-unquantized-assistant
Gemma 4 31B is Google DeepMind's open-weight instruction-tuned multimodal LLM supporting text and image inputs. This variant is a quantization-aware trained (QA…
gemma-4-E2B-it-assistant
Gemma 4 E2B-it-assistant is a 2.3B-parameter instruction-tuned language model from Google DeepMind designed for on-device and edge deployment. It supports text,…
gemma-4-E4B-it-assistant
Gemma 4 E4B is a 4.5B-parameter instruction-tuned open model from Google DeepMind that handles text, images, and audio. It supports 128K-token context, offers r…
gemma-4-E4B-it-OBLITERATED
Gemma 4 E4B OBLITERATED is a 7.996B parameter instruction-tuned model derived from Google's Gemma 4 E4B, with safety guardrails surgically removed via the OBLIT…
GLM-4-32B-0414.w4a16-gptq
GLM-4-32B-0414.w4a16-gptq is a 4-bit quantized version of the GLM-4 32B language model, designed to run on consumer-grade GPUs (32GB+ VRAM). It trades some accu…
GLM-4.5
GLM-4.5 is a 355-billion-parameter open-source LLM from Zhipu AI (zai-org) released under MIT license. It features a mixture-of-experts architecture with 32B ac…
GLM-4.5-Air
GLM-4.5-Air is a 106-billion-parameter open-source language model from Zhipu AI with 12 billion active parameters. It supports both reasoning and immediate-resp…
GLM-4.5-Air-AWQ-4bit
GLM-4.5-Air-AWQ-4bit is a quantized 18.6B-parameter language model from Zhipu AI's GLM-4.5 series, released under the MIT license. It is a mixture-of-experts (M…
GLM-4.5-Air-FP8
GLM-4.5-Air-FP8 is a 106B-parameter mixture-of-experts (MoE) model with 12B active parameters, quantized to FP8 precision for reduced memory footprint. It suppo…
GLM-4.7
GLM-4.7 is a 358B parameter open-source language model optimized for coding, agentic tasks, and complex reasoning. Licensed under MIT, it is freely available fo…
GLM-4.7-AWQ
GLM-4.7-AWQ is a 358B-parameter quantized version of the GLM-4.7 large language model, optimized for coding, reasoning, and agentic tasks. It uses 4-bit AWQ qua…
GLM-4.7-Flash
GLM-4.7-Flash is a 30-billion-parameter open-source mixture-of-experts (MoE) language model designed for efficient deployment. It balances performance with comp…
GLM-4.7-Flash-FP8-Dynamic
GLM-4.7-Flash is a 30-billion parameter mixture-of-experts (MoE) model optimized for efficient inference. This version is quantized to FP8 precision by Unsloth,…
GLM-4.7-Flash-GGUF
GLM-4.7-Flash is a 30B-parameter mixture-of-experts (MoE) language model from Z.ai, quantized and distributed by Unsloth in GGUF format. It supports text genera…
GLM-4.7-Flash-MLX-6bit
GLM-4.7-Flash-MLX-6bit is a 30B-parameter conversational LLM quantized to 6-bit precision for Apple Silicon using MLX. It supports English and Chinese, runs loc…
GLM-4.7-Flash-MLX-8bit
GLM-4.7-Flash-MLX-8bit is a 29.9B parameter language model quantized to 8-bit precision using Apple's MLX framework, optimized for Apple Silicon devices. It is …
GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF
GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF is a 30B mixture-of-experts model with ~2B active parameters, quantized for CPU/GPU inference. It is …
GLM-4.7-FP8
GLM-4.7-FP8 is a 358B-parameter open-source language model from zai-org optimized for coding, tool use, and complex reasoning tasks. It is quantized to 8-bit fl…
GLM-5
GLM-5 is a 744-billion-parameter open-source LLM developed by zai-org, released in April 2026. It uses sparse mixture-of-experts (MoE) architecture with 40B act…
GLM-5.1
GLM-5.1 is a 753B-parameter open-source LLM from zai-org optimized for agentic tasks, coding, and tool use. It is distributed under the MIT license without gati…
GLM-5.1-FP8
GLM-5.1-FP8 is a 754B-parameter open-source language model optimized for agentic and coding tasks. It achieves state-of-the-art performance on code-generation b…
GLM-5.1-GGUF
GLM-5.1 is an open-source large language model optimized for agentic tasks and software engineering. It is distributed as a GGUF quantized variant by Unsloth an…
GLM-5.1-NVFP4
GLM-5.1-NVFP4 is a quantized 754B-parameter mixture-of-experts language model from NVIDIA, optimized for inference on NVIDIA Blackwell GPUs. It uses 4-bit FP4 q…
GLM-5.2
GLM-5.2 is a 753B-parameter open-source language model from zai-org with MIT licensing. It supports 1M-token context and is designed for long-horizon reasoning,…
GLM-5.2-FP8
GLM-5.2-FP8 is an open-source, MIT-licensed large language model from zai-org featuring 753B parameters and claimed 1M-token context support. It emphasizes long…
GLM-5.2-GGUF
GLM-5.2 is an MIT-licensed open-source large language model from Unsloth/Zai-org with a 1M-token context window. It targets reasoning, coding, and agentic tasks…
GLM-5.2-Int4-Int8Mix
GLM-5.2-Int4-Int8Mix is a quantized version of the GLM-5.2 large language model (785B parameters) optimized for vLLM inference. It uses mixed INT4/INT8 quantiza…
GLM-5.2-NVFP4
GLM-5.2-NVFP4 is a 753B-parameter Mixture-of-Experts language model quantized to 4-bit FP4 precision by NVIDIA. It activates 40B parameters at inference and sup…
GLM-5-FP8
GLM-5-FP8 is a 753B-parameter open-source LLM from zai-org, released April 2026. It uses sparse mixture-of-experts architecture with FP8 quantization to reduce …
GLM-5-NVFP4
GLM-5-NVFP4 is a 435B-parameter (40B activated) mixture-of-experts language model quantized to NVIDIA's FP4 format. It is a production-ready quantization of ZAI…
gpt_bigcode-santacoder
SantaCoder is a 1.1B-parameter code generation model trained on 236B tokens of GitHub source code in Python, Java, and JavaScript. It uses a GPT-2 architecture …
gpt-j-6b
GPT-J 6B is a 6-billion-parameter open-source language model trained by EleutherAI on the Pile dataset. It generates text from prompts and performs well on benc…
gpt-neo-1.3B
GPT-Neo 1.3B is a 1.3-billion-parameter open-source language model trained by EleutherAI on the Pile dataset. It replicates GPT-3 architecture and is designed f…
gpt-neo-125m
GPT-Neo 125M is a 150M-parameter open-source language model trained by EleutherAI on the Pile dataset. It replicates the GPT-3 architecture at a much smaller sc…
gpt-neo-2.7B
GPT-Neo 2.7B is a 2.7-billion-parameter open-source language model trained by EleutherAI on the Pile dataset. It replicates the GPT-3 architecture and excels at…
gpt-neox-20b
GPT-NeoX-20B is a 20 billion parameter open-source language model trained on the Pile dataset by EleutherAI. It is designed for research and can be fine-tuned f…
gpt-neox-japanese-2.7b
gpt-neox-japanese-2.7b is a 2.7 billion parameter Japanese language model trained by ABEJA on public Japanese corpora (CC-100, Wikipedia, OSCAR). It is released…
gpt-oss-120b
gpt-oss-120b is OpenAI's 120-billion-parameter open-weight model released under Apache 2.0. It uses mixture-of-experts (MoE) with 5.1B active parameters, runs o…
gpt-oss-120b-GGUF
gpt-oss-120b is OpenAI's open-weight 120B parameter model (5.1B active via MoE) released under Apache 2.0. It is a GGUF-quantized version maintained by Unsloth,…
gpt-oss-120b-MLX-8bit
gpt-oss-120b-MLX-8bit is a 116.8B parameter quantized version of OpenAI's GPT-OSS model, optimized for Apple Silicon using 8-bit MLX quantization. It is an open…
gpt-oss-20b
gpt-oss-20b is OpenAI's 21B-parameter open-weight language model designed for lower-latency and local deployment. It uses a mixture-of-experts architecture with…
gpt-oss-20b-BF16
gpt-oss-20b is OpenAI's 21-billion-parameter open-weight model designed for lower-latency reasoning tasks, local deployment, and specialized use cases. It uses …
gpt-oss-20b-GGUF
gpt-oss-20b is OpenAI's open-weight 20-billion-parameter model optimized for lower latency and local deployment. It uses native MXFP4 quantization and fits in 1…
gpt-oss-20b-MXFP4-Q8
A 20B-parameter quantized version of OpenAI's GPT-OSS-20B model, converted to MLX format for efficient inference on Apple Silicon and compatible hardware. Suppo…
gpt-oss-20b-speculator.eagle3
This is a speculator model (854M parameters) designed to accelerate inference of openai/gpt-oss-20b using EAGLE-3 speculative decoding. It predicts multiple fut…
gpt-oss-20b-unsloth-bnb-4bit
gpt-oss-20b is a 20-billion-parameter open-weight model from OpenAI, optimized for lower latency and local/specialized deployments. It uses a mixture-of-experts…
gpt-oss-safeguard-20b
gpt-oss-safeguard-20b is a 21B-parameter safety-focused language model from OpenAI designed to classify and reason about content safety. It interprets user-prov…
gpt2
GPT-2 is a 124M-parameter open-source language model trained on English internet text. It generates coherent text from prompts and can be fine-tuned for downstr…
gpt2-large
GPT-2 Large is a 774M-parameter open-source transformer language model trained on English web text. It generates coherent text continuations and is widely used …
gpt2-medium
GPT-2 Medium is a 355M-parameter transformer language model released by OpenAI for text generation tasks. It is openly available under MIT license, ungated, and…
gpt2-mini
GPT-2 Mini is a 38.6M-parameter text-generation model pretrained on OpenWebText. Designed for research and education in resource-constrained environments, it ge…
gpt2-xl
GPT-2 XL is a 1.5B parameter open-source transformer language model trained by OpenAI on English web text. It generates text continuations and can be fine-tuned…
granite-3.0-1b-a400m-base
Granite-3.0-1B-A400M-Base is a 1.3B parameter decoder-only language model from IBM, released October 2024. It uses a sparse Mixture of Experts architecture with…
granite-3.0-8b-instruct
Granite-3.0-8B-Instruct is an 8-billion parameter instruction-tuned language model from IBM designed for general-purpose AI assistants. It supports 12 languages…
granite-3.1-2b-instruct-quantized.w4a16
granite-3.1-2b-instruct-quantized.w4a16 is a 2.7B parameter language model optimized for inference via INT4 weight quantization. It reduces memory footprint by …
granite-3.1-8b-instruct
Granite-3.1-8B-Instruct is an 8-billion parameter instruction-tuned language model developed by IBM. It is designed for general-purpose AI assistants and suppor…
granite-3.3-8b-instruct
Granite-3.3-8B-Instruct is an 8-billion parameter instruction-tuned language model from IBM released in April 2025. It supports 128K context length and is optim…
granite-4.0-h-small
Granite-4.0-H-Small is a 32-billion-parameter instruction-tuned language model from IBM, released October 2025. It supports 12 languages, handles general NLP ta…
granite-4.0-h-tiny
Granite-4.0-H-Tiny is a 7B-parameter instruction-tuned LLM from IBM released in October 2025. It supports 12 languages and is designed for enterprise applicatio…
granite-4.0-micro
Granite-4.0-Micro is a 3.4B parameter instruction-tuned language model from IBM released in October 2025. It supports 12 languages and is designed for enterpris…
granite-4.0-tiny-preview
Granite-4.0-Tiny-Preview is a 6.7B parameter open-source LLM from IBM with mixture-of-experts (MoE) architecture, released May 2025. It is instruction-tuned and…
granite-4.1-30b
Granite-4.1-30B is a 30 billion parameter open-source LLM from IBM, released April 2026, optimized for instruction-following, tool calling, and multilingual tas…
granite-4.1-3b
Granite-4.1-3B is a 3.4B parameter instruction-tuned language model from IBM designed for general-purpose text generation, tool calling, and chat applications. …
granite-4.1-3b-GGUF
Granite 4.1 3B is a lightweight, open-source language model from IBM available in GGUF format with multiple quantization options. It is designed for text genera…
granite-4.1-8b
Granite-4.1-8B is an 8-billion-parameter instruction-tuned language model from IBM, released April 2026. It supports 12 languages, excels at tool-calling, summa…
granite-docling-258M
Granite Docling 258M is a lightweight multimodal model (258M parameters) designed to convert document images into structured text and markdown. Built by IBM Res…
granite-guardian-4.1-8b
Granite Guardian 4.1 8B is a specialized safety and evaluation model designed to judge whether LLM inputs and outputs meet specified criteria. It includes pre-b…
GritLM-7B-vllm
GritLM-7B-vllm is a 7-billion-parameter language model based on Mistral that combines text generation and embedding capabilities in a single model. This fork is…
grok-1
Grok-1 is a PyTorch conversion of xAI's open-weights language model, maintained by hpcai-tech. It is a text-generation model released under Apache 2.0 license w…
gte-Qwen2-1.5B-instruct
gte-Qwen2-1.5B-instruct is a lightweight, multilingual text embedding model built on Qwen2-1.5B that generates 1536-dimensional embeddings for sentence similari…
gte-Qwen2-7B-instruct
gte-Qwen2-7B-instruct is a 7-billion-parameter text embedding model fine-tuned on Qwen2-7B for multilingual sentence similarity and semantic search tasks. It ra…
h2ovl-mississippi-2b
h2ovl-mississippi-2b is a 2.1B-parameter vision-language model by H2O.ai designed for multimodal tasks like image captioning, visual question answering, documen…
h2ovl-mississippi-800m
H2OVL-Mississippi-800M is a compact vision-language model (826M parameters) from H2O.ai optimized for OCR, document understanding, and multimodal tasks. It bala…
HarmBench-Llama-2-13b-cls
HarmBench-Llama-2-13b-cls is a 13B parameter classifier model designed to detect harmful behaviors in LLM outputs. It evaluates text against specified harmful b…
Hermes-4-14B
Hermes-4-14B is an open-source, 14-billion-parameter language model fine-tuned from Qwen 3 14B by Nous Research. It emphasizes reasoning capabilities, structure…
Hermes-4-14B-AWQ-4bit
Hermes-4-14B-AWQ-4bit is a quantized (4-bit) version of Nous Research's Hermes 4 reasoning model, based on Qwen 3 14B. It supports hybrid reasoning with explici…
Huihui-gpt-oss-20b-BF16-abliterated
Huihui-gpt-oss-20b-BF16-abliterated is a 20B parameter open-source LLM derived from unsloth/gpt-oss-20b-BF16 with safety filters removed via abliteration. It is…
Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP
Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP is a 27-billion-parameter text-generation model optimized for deployment on 4× NVIDIA Blackwell GPUs. It combines hybri…
Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
This is a 35.9B parameter mixture-of-experts model derived from Qwen 3.6, fine-tuned with Claude 4.7 reasoning distillation and then modified ('abliterated') to…
Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF
Huihui-Qwythos-9B is a 9-billion-parameter LLM based on Qwen3.5, quantized to GGUF format for efficient local deployment. It is an 'abliterated' (safety-reduced…
Hy-MT2-1.8B
Hy-MT2 is a lightweight multilingual translation model from Tencent available in three sizes (1.8B, 7B, 30B-A3B MoE). It supports translation across 33 language…
Hy-MT2-30B-A3B
Hy-MT2-30B-A3B is a 30-billion parameter mixture-of-experts (MoE) multilingual translation model from Tencent, released May 2026. It supports 33 languages and i…
indic-parler-tts
Indic Parler TTS is an open-source text-to-speech model trained by AI4Bharat for Indian languages. It converts text to speech across 22 languages including Hind…
Intern-S1-Pro
Intern-S1-Pro is a trillion-parameter multimodal language model optimized for scientific reasoning, image-text understanding, and general text generation. It us…
internlm3-8b-instruct
InternLM3-8B-Instruct is an 8-billion parameter instruction-tuned language model developed by Shanghai AI Laboratory. It is open-source under Apache 2.0, ungate…
Jan-nano-128k
Jan-Nano-128k is a 4B-parameter open-source language model from Menlo Research with a native 128k token context window, designed for research and document analy…
Jan-v3.5-4B-gguf
Jan-v3.5-4B is a 4-billion-parameter open-source LLM fine-tuned for math reasoning and conversational personality. It runs locally via vLLM or llama.cpp, uses A…
japanese-gpt-neox-small
japanese-gpt-neox-small is a lightweight, open-source Japanese language model with 203M parameters trained on Japanese CC-100, Wikipedia, and MC4 datasets. It u…
Karnak-40B-v1.0
Karnak-40B is a 40-billion-parameter open-source LLM optimized for Arabic and English, built by extending and fine-tuning Qwen3-30B. It supports up to 20K token…
Kimi-Linear-48B-A3B-Instruct
Kimi-Linear-48B-A3B-Instruct is a 48-billion parameter open-source language model from Moonshot AI that uses a hybrid linear attention architecture (Kimi Delta …
Laguna-XS.2
Laguna-XS.2 is a 33B-parameter Mixture-of-Experts model with 3B activated parameters per token, designed for code-generation and agentic tasks that run locally.…
LightOnOCR-1B-1025
LightOnOCR-1B is a compact vision-language model (1.16B parameters) specialized in extracting text from documents, PDFs, forms, and tables. It runs 2–5× faster …
LightOnOCR-2-1B
LightOnOCR-2-1B is a 1-billion-parameter vision-language model designed for document OCR and text extraction from PDFs, scans, and images. It processes document…
Ling-lite-1.5
Ling-lite-1.5 is a 16.8B-parameter open-source MoE (Mixture of Experts) language model with 2.75B activated parameters per forward pass, developed by InclusionA…
Ling-mini-2.0
Ling-mini-2.0 is a 16.3B parameter sparse Mixture-of-Experts (MoE) model that activates only 1.4B parameters per token, achieving performance comparable to 7–8B…
DeepSeek-R1-Distill-Qwen-1.5B
DeepSeek-R1-Distill-Qwen-1.5B is a 1.5 billion parameter language model optimized for on-device deployment on Android, iOS, and web platforms via Google's LiteR…
Qwen2.5-1.5B-Instruct
Qwen2.5-1.5B-Instruct is a 1.5 billion parameter instruction-tuned language model optimized for edge deployment on Android and iOS via Google's LiteRT (formerly…
Qwen3-0.6B
Qwen3-0.6B is a 600M-parameter open-source language model optimized for on-device deployment via LiteRT-LM. It is available in multiple quantized formats (INT8,…
LLaDA-1.5
LLaDA-1.5 is an 8B-parameter open-source language model trained using variance-reduced preference optimization (VRPO). It is MIT-licensed, ungated, and designed…
LLaDA-8B-Base
LLaDA-8B-Base is an 8-billion-parameter language model built from scratch using diffusion-based training. It claims performance comparable to Meta's LLaMA3 8B. …
LLaDA-8B-Instruct
LLaDA-8B-Instruct is an 8-billion-parameter instruction-tuned language model trained from scratch using diffusion-based training. It is positioned as a performa…
LLaDA2.0-mini
LLaDA2.0-mini is a 16-billion-parameter open-source language model built on a Mixture-of-Experts (MoE) diffusion architecture. It activates only ~1.4B parameter…
LLaDA2.1-flash
LLaDA2.1-flash is a 102.9B-parameter diffusion language model from inclusionAI featuring dual inference modes (Speed and Quality). It trades off latency vs. acc…
llama-160m
llama-160m is a lightweight, 160-million-parameter language model trained on Wikipedia and portions of C4 datasets. It was developed primarily as a speculative …
LLaMA-1B-dj-refine-150B
LLaMA-1B-dj-refine is a 1.3B parameter open-source language model trained on 150B tokens of curated RedPajama and Pile data using the Data-Juicer refinement pip…
Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF
A Llama 3.2 mixture-of-experts model (18.4B parameters from 8×3B experts) optimized for creative writing, fiction, and roleplay. Runs at 50+ tokens/sec on low-e…
llama-68m
llama-68m is a lightweight 68-million-parameter language model trained on Wikipedia and C4 datasets. It is designed as a speculative decoding assistant in the S…
llava-onevision-qwen2-7b-ov
LLaVA-OneVision is a 7B-parameter multimodal language model that processes text, images, multiple images, and videos. Based on Qwen2, it supports English and Ch…
llm-jp-3-150m
llm-jp-3-150m is a 150M-parameter transformer language model developed by Japan's National Institute of Informatics, pre-trained on 2.1T tokens of Japanese, Eng…
llm-jp-4-32b-a3b-thinking
llm-jp-4-32b-a3b-thinking is a 32-billion parameter mixture-of-experts language model developed by Japan's National Institute of Informatics. It supports Japane…
llm-jp-4-8b-thinking
llm-jp-4-8b-thinking is an 8-billion-parameter open-source language model developed by Japan's National Institute of Informatics. It is bilingual (Japanese and …
LongCat-Flash-Chat
LongCat-Flash-Chat is a 560-billion-parameter open-source language model developed by Meituan using a Mixture-of-Experts (MoE) architecture. It dynamically acti…
GLM-5.2-NVFP4
GLM-5.2-NVFP4 is a 744B-parameter Mixture-of-Experts language model quantized to 4-bit precision using NVIDIA's tooling. It uses only 40B active parameters per …
lumeleto
Lumeleto is a 124M-parameter fine-tuned GPT-2/Falcon model released by gratefulasi under MIT license. It is ungated and available for immediate use. The model i…
lynx-instruct-30b
Lynx Instruct 30B is a European-focused, multilingual large language model built on Qwen3's efficient Mixture-of-Experts architecture. It activates ~3B of its 3…
macbert4csc-base-chinese
MacBERT4CSC is a specialized Chinese spelling correction model with 102M parameters, fine-tuned for detecting and correcting character-level errors in Chinese t…
Mamba2-primed-HQwen3-8B-Instruct
Mamba2-primed-HQwen3-8B-Instruct is an 8B-parameter hybrid language model that combines traditional Attention layers with State-Space Model (Mamba-2) layers in …
Midm-2.0-Mini-Instruct
Midm-2.0-Mini-Instruct is a 2.3B parameter language model developed by K-intelligence (a KT subsidiary) optimized for Korean language understanding and Korean c…
MiMo-7B-Base
MiMo-7B-Base is a 7.8B parameter open-source language model developed by Xiaomi, pre-trained from scratch on ~25 trillion tokens with a focus on reasoning tasks…
MiMo-7B-RL
MiMo-7B-RL is a 7.8B-parameter open-source language model developed by Xiaomi, optimized for mathematical reasoning and code generation through a combination of…
MiMo-V2.5-Pro
MiMo-V2.5-Pro is a 1.02T-parameter Mixture-of-Experts language model from Xiaomi with 42B active parameters, designed for long-context reasoning (up to 1M token…
MiMo-V2.5-Pro-FP4-DFlash
MiMo-V2.5-Pro-FP4-DFlash is a 1-trillion-parameter mixture-of-experts language model from Xiaomi optimized for fast inference. It uses FP4 quantization on its e…
MiMo-V2-Flash
MiMo-V2-Flash is a 309B-parameter mixture-of-experts language model from Xiaomi with only 15B active parameters, designed for fast inference and agentic reasoni…
MiniCPM3-4B
MiniCPM3-4B is a 4-billion-parameter open-source language model from OpenBMB designed for efficient inference while maintaining competitive performance on Engli…
MiniCPM4-0.5B
MiniCPM4-0.5B is a 433M-parameter language model optimized for edge deployment. It supports conversational text generation in Chinese and English, trained on 1 …
MiniCPM4.1-8B
MiniCPM4.1-8B is an 8-billion-parameter open-source language model from OpenBMB designed for efficient inference on consumer hardware. It combines sparse attent…
MiniCPM5-1B
MiniCPM5-1B is a 1.08-billion-parameter open-source language model optimized for on-device deployment and resource-constrained environments. It supports a 131K-…
MiniMax-M1-40k
MiniMax-M1-40k is a 456B-parameter dense language model released by MiniMaxAI under the Apache 2.0 license. It is ungated and designed for text generation tasks…
MiniMax-M2.7-REAP-172B-A10B-NVFP4-GB10
MiniMax-M2.7-REAP-172B is a 172B-parameter mixture-of-experts language model, heavily quantized to ~92 GiB for deployment on NVIDIA Blackwell (GB10) GPUs. The m…
Mistral-7B-Instruct-v0.2
Mistral-7B-Instruct-v0.2 is a 7.2B parameter instruction-tuned language model from Mistral AI, designed for conversational tasks. It offers a 32k context window…
Mistral-7B-Instruct-v0.1
Mistral-7B-Instruct-v0.1 is a 7-billion-parameter instruction-tuned LLM released by Mistral AI under Apache 2.0 license. It is a fine-tuned variant of the base …
Mistral-7B-Instruct-v0.1-GGUF
Mistral-7B-Instruct-v0.1-GGUF is a quantized version of Mistral AI's 7-billion parameter instruction-tuned language model, packaged in GGUF format for efficient…
Mistral-7B-Instruct-v0.2
Mistral-7B-Instruct-v0.2 is a 7-billion-parameter instruction-tuned language model from Mistral AI, designed for conversational tasks. It supports a 32k context…
Mistral-7B-Instruct-v0.2-AWQ
Mistral-7B-Instruct-v0.2-AWQ is a 4-bit quantized version of Mistral AI's 7 billion parameter instruction-tuned language model. The quantization (AWQ) reduces m…
Mistral-7B-Instruct-v0.2-GGUF
Mistral-7B-Instruct-v0.2-GGUF is a quantized version of Mistral AI's 7-billion-parameter instruction-tuned language model, optimized for CPU and GPU inference v…
mistral-7b-instruct-v0.3-bnb-4bit
Mistral 7B Instruct v0.3 quantized to 4-bit by Unsloth is a 7.5B-parameter instruction-tuned language model optimized for memory efficiency and faster fine-tuni…
Mistral-7B-Instruct-v0.3-GGUF
Mistral-7B-Instruct-v0.3-GGUF is a quantized version of Mistral's 7-billion-parameter instruction-tuned language model, optimized for CPU and GPU inference via …
Mistral-7B-v0.1
Mistral-7B-v0.1 is a 7-billion-parameter open-source language model that generates text. It uses modern transformer techniques (grouped-query attention, sliding…
mistral-7b-v0.3-bnb-4bit
Mistral 7B v0.3 quantized to 4-bit by Unsloth is a 7.5B parameter base language model optimized for memory efficiency and fine-tuning speed. The quantization re…
Mistral-Small-24B-Instruct-2501-AWQ
Mistral-Small-24B-Instruct-2501-AWQ is a 24 billion parameter instruction-tuned language model from Mistral AI, quantized to 4-bit INT4 by stelterlab using Auto…
Mistral-Small-24B-Instruct-2501-GGUF
Mistral-Small-24B-Instruct-2501-GGUF is a quantized version of Mistral AI's 24B instruction-tuned model, converted to GGUF format for efficient local inference.…
Mixtral-8x22B-v0.1-GGUF
Mixtral-8x22B-v0.1-GGUF is a quantized, community-distributed version of Mistral AI's Mixtral 8x22B mixture-of-experts model. It is a 176B parameter model with …
Mixtral-8x7B-Instruct-v0.1
Mixtral-8x7B-Instruct is a 46.7B parameter open-source language model using a Sparse Mixture of Experts (SMoE) architecture. It is instruction-tuned, multilingu…
MobileLLaMA-1.4B-Chat
MobileLLaMA-1.4B-Chat is a lightweight 1.4 billion parameter language model optimized for mobile and edge devices. It was fine-tuned from a base model using ins…
moondream2
Moondream2 is a lightweight vision-language model (~1.9B parameters) designed for efficient multimodal reasoning on images. It supports image captioning, visual…
Moonlight-16B-A3B
Moonlight-16B-A3B is a 16-billion-parameter mixture-of-experts (MoE) language model trained with the Muon optimizer on 5.7 trillion tokens. It activates 3 billi…
Moonlight-16B-A3B-Instruct
Moonlight-16B-A3B-Instruct is a 16-billion-parameter mixture-of-experts (MoE) language model with 3B activated parameters per token, trained using the Muon opti…
mxbai-rerank-base-v2
mxbai-rerank-base-v2 is a 494M-parameter text ranking model from mixedbread-ai, licensed under Apache 2.0 and available ungated. It ranks documents/passages by …
mxbai-rerank-large-v2
mxbai-rerank-large-v2 is a 1.5B-parameter text ranking model from mixedbread-ai, distributed under Apache 2.0. It ranks relevance of text passages to queries an…
mzansilm-125m
MzansiLM is a 125M-parameter decoder-only language model trained on MzansiText, a multilingual corpus covering all eleven official South African languages (Afri…
neutrino-instruct
Neutrino-Instruct is a 7B-parameter instruction-tuned LLM designed for conversational AI, multi-step reasoning, and instruction-following tasks. It runs locally…
Nex-N2-mini-AWQ-INT4
Nex-N2-mini is a 37B parameter quantized (INT4 AWQ) agentic language model built on Qwen3.5-35B, designed for tool calling, code generation, and long-horizon ta…
North-Mini-Code-1.0
North Mini Code is a 30B-parameter sparse mixture-of-experts model from Cohere Labs optimized for code generation and agentic software engineering tasks. It use…
North-Mini-Code-1.0-GGUF
North-Mini-Code-1.0-GGUF is a quantized version of Cohere's North-Mini-Code model, optimized for local inference via llama.cpp. It is a code-capable conversatio…
Qwen3.5-122B-A10B-NVFP4
Qwen3.5-122B-A10B-NVFP4 is NVIDIA's quantized version of Alibaba's 122B mixture-of-experts language model. It reduces model size and GPU memory by ~4x using NVF…
OLMo-1B-hf
OLMo-1B is a 1.2B-parameter open-source language model trained by Allen Institute for AI on 3 trillion tokens. It is released under Apache 2.0, meaning code and…
OLMo-2-0425-1B
OLMo 2 1B is a 1.5B-parameter open-source language model from Allen Institute for AI, trained on 4 trillion tokens with a 4096-token context window. It is avail…
OLMo-2-0425-1B-Instruct
OLMo-2-0425-1B-Instruct is a 1.5B-parameter open-source language model developed by Allen Institute for AI, fine-tuned for instruction-following and conversatio…
OLMo-2-1124-7B-Instruct
OLMo-2-1124-7B-Instruct is a 7.3B-parameter open-source language model from Allen Institute for AI, fine-tuned for instruction-following and chat. It uses Apach…
Olmo-3-1025-7B
OLMo 3 7B is an open-weight, 7.3B-parameter language model trained by Allen Institute for AI on 5.93 trillion tokens. It supports a 65,536-token context window …
Olmo-3-1125-32B
Olmo 3 32B is an open-weight base language model with 32 billion parameters, trained by Allen Institute for AI on 5.5 trillion tokens. It supports a 65,536-toke…
Olmo-3-7B-Instruct
Olmo-3-7B-Instruct is a 7.3B parameter open-source language model from Allen Institute for AI, optimized for instruction-following and multi-turn conversation. …
Olmo-3-7B-Instruct-SFT
Olmo-3-7B-Instruct-SFT is a 7-billion parameter open-source language model developed by Allen Institute for AI. It is a supervised fine-tuned (SFT) variant desi…
Olmo-3-7B-Think
Olmo-3-7B-Think is a 7.3B parameter open-source language model from Allen Institute for AI, designed for reasoning-heavy tasks like math and coding. It uses a c…
OLMo-7B
OLMo 7B is an open-source 7-billion-parameter transformer language model trained by the Allen Institute for AI on 2.5 trillion tokens. It is released under Apac…
Olmo-Hybrid-7B
Olmo-Hybrid-7B is a 7-billion-parameter open-source language model from Allen Institute for AI that combines traditional transformer attention with a novel RNN-…
OLMoE-1B-7B-0125-Instruct
OLMoE-1B-7B-0125-Instruct is an open-source mixture-of-experts language model (6.9B parameters) from Allen Institute for AI, released January 2025. It is instru…
OLMoE-1B-7B-0924
OLMoE-1B-7B is an open-source Mixture-of-Experts language model with 1B active parameters (7B total) released by Allen Institute for AI in September 2024. It ac…
OLMoE-1B-7B-0924-Instruct
OLMoE-1B-7B-0924-Instruct is an open-source Mixture-of-Experts language model with 1 billion active parameters (7 billion total) released by Allen AI in Septemb…
open_llama_7b
OpenLLaMA 7B is an open-source, Apache 2.0-licensed reproduction of Meta's LLaMA model, trained on the RedPajama dataset (1T tokens). It offers comparable perfo…
openai-gpt
OpenAI GPT-1 is a 120M-parameter transformer-based language model from 2018, the first in OpenAI's GPT series. It performs causal language modeling and can be f…
OpenReasoning-Nemotron-32B
OpenReasoning-Nemotron-32B is a 32.7B parameter reasoning LLM built on Qwen2.5-32B, fine-tuned for math, code, and science problem-solving. It demonstrates stro…
OpenThinker2-7B
OpenThinker2-7B is a 7.6B-parameter open-source reasoning model fine-tuned from Qwen2.5-7B-Instruct on a 1M-example dataset focused on math, code, and reasoning…
OpenThinker3-7B
OpenThinker3-7B is a 7.6B-parameter open-source reasoning model fine-tuned from Qwen2.5-7B-Instruct on 1.2M synthetic reasoning examples (math, code, science). …
Qwen2.5-Coder-7B-Instruct-AWQ
Qwen2.5-Coder-7B-Instruct-AWQ is a 7.6B parameter code-focused language model from Alibaba's Qwen team, quantized to 4-bit precision using AWQ for reduced memor…
Ornith-1.0-35B
Ornith-1.0-35B is a 35B-parameter open-source LLM optimized for agentic coding tasks. It uses a mixture-of-experts (MoE) architecture built on Qwen 3.5 and empl…
Ornith-1.0-35B-FP8
Ornith-1.0-35B is a 35-billion-parameter open-source LLM optimized for agentic coding tasks. It is post-trained on Qwen 3.5 and uses a mixture-of-experts (MoE) …
Ornith-1.0-35B-GGUF
Ornith-1.0-35B is a 35-billion-parameter open-source language model optimized for coding tasks and agentic workflows. Built on Qwen 3.5 and Gemma 4 foundations,…
Ornith-1.0-35B-MTP-APEX-GGUF
Ornith-1.0-35B-MTP-APEX-GGUF is a 35B-parameter mixture-of-experts LLM optimized for agentic coding tasks. It is derived from Qwen3.5 and trained with reinforce…
Ornith-1.0-35B-NVFP4-MTP-GGUF
Ornith-1.0-35B-NVFP4-MTP-GGUF is a 35-billion-parameter mixture-of-experts (MoE) language model optimized for NVIDIA Blackwell GPUs. It combines aggressive 4-bi…
Ornith-1.0-397B
Ornith-1.0-397B is a 397-billion-parameter open-source language model optimized for agentic coding tasks. Built on Qwen 3.5 and Gemma 4 foundations, it uses a m…
Ornith-1.0-397B-FP8
Ornith-1.0-397B is a 397-billion-parameter open-source LLM optimized for agentic coding tasks. It uses a mixture-of-experts (MoE) architecture built on Qwen 3.5…
Ornith-1.0-9B
Ornith-1.0-9B is a 9-billion-parameter open-source language model designed for agentic coding tasks. It was fine-tuned on Qwen 3.5 using reinforcement learning …
Ornith-1.0-9B-GGUF
Ornith-1.0-9B-GGUF is a 9-billion-parameter open-source coding agent model released by deepreinforce-ai under MIT license. It is optimized for code generation a…
Ornith-1.0-9B-MTP-GGUF
Ornith-1.0-9B-MTP-GGUF is a 9-billion-parameter language model quantized in GGUF format with a built-in multi-token prediction (MTP) head for speculative decodi…
OTel-LLM-8B-A1B-IT
OTel-LLM-8B-A1B-IT is an 8-billion-parameter language model fine-tuned on telecommunications domain data. It is designed specifically for retrieval-augmented ge…
OTel-LLM-E4B-IT
OTel-LLM-E4B-IT is a 4.5B-parameter language model fine-tuned on telecommunications domain data for context-grounded answer generation in RAG (Retrieval-Augment…
Ouro-1.4B
Ouro-1.4B is a 1.4 billion parameter language model from ByteDance that uses iterative recurrent computation to match the performance of larger 3–4B models. It …
Ovis1.6-Gemma2-9B
Ovis1.6-Gemma2-9B is a 10B-parameter open-source multimodal LLM that processes both images and text. It uses a Siglip-400M vision encoder paired with Gemma2-9B-…
Ovis1.6-Llama3.2-3B
Ovis1.6-Llama3.2-3B is a 3B-parameter multimodal large language model (MLLM) that processes images and text together. It combines a Siglip-400M vision encoder w…
Ovis2-1B
Ovis2-1B is a lightweight multimodal language model (1.27B parameters) that processes images, text, and video to generate text responses. It uses a Qwen2.5-0.5B…
Ovis2.5-9B
Ovis2.5-9B is a 9-billion-parameter open-source multimodal large language model (MLLM) that processes both images and text. It uses a native-resolution vision t…
PARD-Llama-3.2-1B
PARD-Llama-3.2-1B is a 1.5B-parameter draft model optimized for speculative decoding—a technique that speeds up LLM inference by generating multiple token candi…
phi-1_5
Phi-1.5 is a 1.3-billion-parameter open-source language model from Microsoft optimized for code generation, QA, and chat tasks. It was trained on 150B tokens us…
phi-2
Phi-2 is a 2.7B parameter open-source language model from Microsoft designed for question-answering, chat, and code generation. It achieves near state-of-the-ar…
Phi-3.5-mini-instruct
Phi-3.5-mini-instruct is a 3.8B parameter instruction-tuned language model from Microsoft, optimized for memory- and compute-constrained environments. It suppor…
Phi-3.5-mini-instruct
Phi-3.5-mini-instruct is a 3.8B parameter open-source LLM from Microsoft, optimized for resource-constrained environments while maintaining strong reasoning cap…
Phi-3.5-mini-instruct-GGUF
Phi-3.5-mini-instruct-GGUF is a quantized version of Microsoft's Phi-3.5-mini-instruct model, optimized for CPU and edge inference via llama.cpp. It offers mult…
Phi-3.5-MoE-instruct
Phi-3.5-MoE is a 41.8B-parameter mixture-of-experts language model from Microsoft with only 6.6B active parameters per token. It is MIT-licensed, ungated, and d…
Phi-3.5-vision-instruct
Phi-3.5-vision-instruct is a lightweight 4.1B-parameter multimodal LLM from Microsoft that processes both text and images. It is designed for memory- and comput…
Phi-3-medium-128k-instruct
Phi-3-Medium-128K-Instruct is a 14 billion parameter open-source language model from Microsoft, optimized for memory-constrained and latency-sensitive deploymen…
Phi-3-mini-128k-instruct
Phi-3-mini-128k-instruct is a 3.8B-parameter instruction-tuned LLM from Microsoft designed for resource-constrained environments. It supports 128K token context…
Phi-3-mini-4k-instruct
Phi-3-mini-4k-instruct is a 3.8B parameter lightweight language model from Microsoft, licensed under MIT. It is designed for memory-constrained and latency-sens…
Phi-3-mini-4k-instruct-gguf
Phi-3-Mini-4K is a 3.8B parameter lightweight language model by Microsoft, available in GGUF quantized format for efficient local deployment. It targets memory-…
Phi-3-vision-128k-instruct
Phi-3-Vision-128K-Instruct is a 4.1B-parameter multimodal language model from Microsoft that processes both text and images. It supports 128K context length and…
phi-4
Phi-4 is a 14B-parameter open-source language model from Microsoft designed for efficiency in memory-constrained and latency-sensitive environments. It excels a…
Phi-4-mini-instruct
Phi-4-mini-instruct is a 3.8B parameter open-source language model from Microsoft designed for memory and compute-constrained environments. It supports 128K tok…
Phi-4-mini-instruct-GGUF
Phi-4-mini-instruct-GGUF is a 3.8B parameter lightweight language model from Microsoft (quantized by Unsloth) designed for memory-constrained and latency-sensit…
Phi-4-mini-reasoning
Phi-4-mini-reasoning is a 3.8B-parameter open-source LLM from Microsoft designed for mathematical reasoning and multi-step logic problems in resource-constraine…
Phi-4-mini-reasoning-MLX-4bit
Phi-4-mini-reasoning-MLX-4bit is a 599M-parameter quantized version of Microsoft's Phi-4-mini-reasoning model, optimized for Apple Silicon and other MLX-compati…
Phi-4-multimodal-instruct
Phi-4-multimodal-instruct is a 5.6B-parameter open-source model from Microsoft that processes text, images, and audio to generate text outputs. It supports 22 l…
Phi-mini-MoE-instruct
Phi-mini-MoE is a lightweight 7.6B-parameter mixture-of-experts language model from Microsoft that activates only 2.4B parameters per inference, designed for me…
Phi-tiny-MoE-instruct
Phi-tiny-MoE is a lightweight 3.8B-parameter mixture-of-experts model from Microsoft with only 1.1B active parameters. It uses a compression technique called Sl…
plamo-2-1b
PLaMo 2 1B is a 1-billion-parameter language model from Preferred Networks trained on 4 trillion tokens of English, Japanese, code, and other data. It uses a Sa…
PowerLM-3b
PowerLM-3B is a 3-billion-parameter language model from IBM Research designed for efficiency and quality in text generation tasks. It uses a specialized trainin…
PowerMoE-3b
PowerMoE-3B is a 3-billion-parameter sparse Mixture-of-Experts language model from IBM Research. It activates only ~800M parameters per token, making it computa…
prometheus-7b-v2.0
Prometheus 2 is a 7B-parameter language model fine-tuned on 300K human feedback examples to act as an automated evaluator for other LLMs. It specializes in both…
pythia-1.4b
Pythia-1.4B is a 1.4 billion parameter open-source language model developed by EleutherAI for interpretability and research. It is licensed under Apache 2.0, no…
pythia-12b
Pythia-12B is a 12-billion parameter open-source language model developed by EleutherAI for interpretability and research purposes. It is trained on the Pile da…
pythia-14m
Pythia-14M is a 14 million parameter transformer-based language model developed by EleutherAI for research purposes. It is a tiny, open-source model trained on …
pythia-160m
Pythia-160M is a 160-million-parameter open-source language model developed by EleutherAI for research into LLM behavior and interpretability. It is trained on …
pythia-160m-deduped
Pythia-160M-deduped is a 160-million-parameter open-source language model from EleutherAI designed primarily for interpretability research. It is a smaller, res…
pythia-1b
Pythia-1B is a 1 billion parameter open-source language model developed by EleutherAI for research purposes. It was trained on the Pile, a 825GB diverse English…
pythia-2.8b
Pythia-2.8B is a 2.8-billion-parameter English-language transformer model released by EleutherAI under Apache 2.0. It was designed primarily for interpretabilit…
pythia-410m
Pythia-410M is a 410-million-parameter open-source language model developed by EleutherAI for interpretability research. It is a causal language model trained o…
pythia-410m-deduped
Pythia-410M-deduped is a 410M-parameter open-source language model developed by EleutherAI for interpretability research. It is trained on a deduplicated versio…
pythia-6.9b
Pythia-6.9B is a 6.9-billion-parameter open-source language model developed by EleutherAI for interpretability research. It generates text in English and is ava…
pythia-70m-deduped
Pythia-70M-deduped is a 70-million-parameter open-source language model from EleutherAI designed for research and interpretability studies. It's a small, lightw…
qmd-query-expansion-1.7B-gguf
QMD Query Expansion is a 1.7B parameter fine-tuned language model designed to expand short search queries into multiple formats (lexical, vector, and hypothetic…
Qwen-AgentWorld-35B-A3B
Qwen-AgentWorld-35B-A3B is a 35-billion parameter language model fine-tuned specifically to simulate agent environments across seven interaction domains (tool c…
Qwen-AgentWorld-35B-A3B-GGUF
Qwen-AgentWorld-35B-A3B is a 35-billion-parameter language model specialized for simulating agent environments across seven domains: tool calling, search, termi…
Qwen2.5-Coder-14B-Instruct-GGUF
Qwen2.5-Coder-14B-Instruct-GGUF is a 14.7 billion parameter code-specialized large language model from Alibaba Cloud's Qwen team, distributed in GGUF quantized …
Qwen3-0.6B-GGUF
Qwen3-0.6B-GGUF is a compact, quantized version of Alibaba's latest 600-million-parameter language model. It supports switching between 'thinking mode' (for rea…
Qwen3-8B-GGUF
Qwen3-8B-GGUF is a quantized, open-source 8.2B-parameter language model by Alibaba's Qwen team. It supports a unique 'thinking mode' for complex reasoning and '…
Qwen2-0.5B
Qwen2-0.5B is a compact, open-source base language model with 494M parameters from Alibaba's Qwen team. It uses modern architecture (Transformer with SwiGLU, gr…
Qwen2-0.5B-Instruct
Qwen2-0.5B-Instruct is a 494M-parameter instruction-tuned language model from Alibaba's Qwen team, optimized for conversational tasks and lightweight deployment…
Qwen2-1.5B
Qwen2-1.5B is a 1.5 billion-parameter base language model released by Qwen (Alibaba) in June 2024. It is Apache 2.0 licensed, ungated, and available via Hugging…
Qwen2-1.5B-Instruct
Qwen2-1.5B-Instruct is a 1.5B parameter instruction-tuned language model from Alibaba's Qwen team. It is designed for conversational AI and text generation task…
Qwen2-1.5B-Instruct-AWQ
Qwen2-1.5B-Instruct-AWQ is a 1.5 billion parameter instruction-tuned language model from Alibaba's Qwen team, quantized to 4-bit using AWQ (Activation-aware Wei…
Qwen2-1.5B-Instruct-FP8
Qwen2-1.5B-Instruct-FP8 is a quantized 1.5-billion-parameter language model optimized for efficient deployment. It uses 8-bit floating-point quantization to red…
Qwen2-1.5B-Instruct-GPTQ-Int4
Qwen2-1.5B-Instruct-GPTQ-Int4 is a 1.5 billion parameter instruction-tuned language model from Qwen, quantized to 4-bit using GPTQ for reduced memory footprint.…
Qwen2.5-0.5B
Qwen2.5-0.5B is a 494M-parameter base language model from Alibaba's Qwen team, released September 2024. It is a pretrained causal model designed for fine-tuning…
Qwen2.5-0.5B-Instruct
Qwen2.5-0.5B-Instruct is a 494M-parameter instruction-tuned language model from Alibaba's Qwen team. It is purpose-built for lightweight deployment, supporting …
Qwen2.5-0.5B-Instruct-GGUF
Qwen2.5-0.5B-Instruct-GGUF is a 490M-parameter instruction-tuned language model from Alibaba's Qwen team, quantized in GGUF format for efficient local deploymen…
Qwen2.5-1.5B
Qwen2.5-1.5B is a 1.5-billion-parameter base language model from Alibaba's Qwen team, released October 2024. It is a causal transformer designed for pretraining…
Qwen2.5-1.5B-Instruct
Qwen2.5-1.5B-Instruct is a 1.5-billion-parameter instruction-tuned language model from Alibaba's Qwen team. It is designed for efficient on-device or self-hoste…
Qwen2.5-1.5B-Instruct-AWQ
Qwen2.5-1.5B-Instruct-AWQ is a 1.5-billion-parameter instruction-tuned language model from Alibaba's Qwen team, compressed using 4-bit AWQ quantization. It is d…
Qwen2.5-1.5B-Instruct-GGUF
Qwen2.5-1.5B-Instruct-GGUF is a 1.5 billion parameter instruction-tuned language model from Alibaba's Qwen team, distributed in GGUF format for efficient local …
Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit
Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit is a 1.5B parameter instruction-tuned language model quantized to 4-bit using bitsandbytes and optimized by Unsloth. It t…
Qwen2.5-1.5B-quantized.w8a8
Qwen2.5-1.5B-quantized.w8a8 is a compressed 1.5B-parameter language model optimized for efficient deployment. Both weights and activations are quantized to 8-bi…
Qwen2.5-1.5B-unsloth-bnb-4bit
Qwen2.5-1.5B is a 1.58B-parameter quantized language model from Alibaba's Qwen team, optimized for speed and memory efficiency using Unsloth's 4-bit quantizatio…
Qwen2.5-14B
Qwen2.5-14B is a 14.7-billion-parameter open-source base language model from Alibaba's Qwen team, released September 2024. It supports 131K token context length…
Qwen2.5-14B-bnb-4bit
Qwen2.5-14B-bnb-4bit is a 14.7B-parameter base language model quantized to 4-bit precision by Unsloth, released under Apache 2.0. It is not intended for direct …
Qwen2.5-14B-Instruct
Qwen2.5-14B-Instruct is a 14.7-billion-parameter instruction-tuned language model from Alibaba Cloud's Qwen team. It supports up to 131K token context length an…
Qwen2.5-14B-Instruct-4bit
Qwen2.5-14B-Instruct-4bit is a 14-billion parameter instruction-tuned language model converted to MLX format (optimized for Apple Silicon). It supports conversa…
Qwen2.5-14B-Instruct-AWQ
Qwen2.5-14B-Instruct-AWQ is a 14.7 billion parameter instruction-tuned language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for reduced memory …
Qwen2.5-14B-Instruct-GGUF
Qwen2.5-14B-Instruct-GGUF is a quantized version of Alibaba's Qwen 2.5 14-billion-parameter instruction-tuned model, optimized for CPU/edge inference via llama.…
Qwen2.5-14B-Instruct-GPTQ-Int4
Qwen2.5-14B-Instruct-GPTQ-Int4 is a 14.7-billion-parameter instruction-tuned language model from Alibaba's Qwen team, compressed to 4-bit GPTQ quantization for …
Qwen2.5-32B-Instruct
Qwen2.5-32B-Instruct is a 32.7 billion parameter instruction-tuned language model from Alibaba's Qwen team, released September 2024. It supports up to 131K toke…
Qwen2.5-32B-Instruct-AWQ
Qwen2.5-32B-Instruct-AWQ is a 32-billion-parameter instruction-tuned language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for reduced memory fo…
Qwen2.5-32B-Instruct-bnb-4bit
Qwen2.5-32B-Instruct-bnb-4bit is a 32.5 billion parameter instruction-tuned LLM from Alibaba, quantized to 4-bit by Unsloth for memory efficiency. It supports u…
Qwen2.5-32B-Instruct-fp8-dynamic
Qwen2.5-32B-Instruct-fp8-dynamic is a 32.5-billion parameter instruction-tuned language model from Alibaba's Qwen team, quantized to FP8 for reduced memory foot…
Qwen2.5-32B-Instruct-GPTQ-Int4
Qwen2.5-32B-Instruct-GPTQ-Int4 is a 32-billion-parameter instruction-tuned language model from Alibaba's Qwen team, compressed to 4-bit using GPTQ quantization.…
Qwen2.5-32B-Instruct-GPTQ-Int8
Qwen2.5-32B-Instruct-GPTQ-Int8 is an Apache 2.0 licensed, 32.5-billion-parameter instruction-tuned LLM from Alibaba's Qwen team, quantized to 8-bit using GPTQ f…
Qwen2.5-3B-Instruct-bnb-4bit
Qwen2.5-3B-Instruct-bnb-4bit is a 3.1B parameter instruction-tuned LLM quantized to 4-bit using bitsandbytes, maintained by Unsloth. It supports 32K context inp…
Qwen2.5-3B-Instruct-unsloth-bnb-4bit
Qwen2.5-3B-Instruct is a 3.2B parameter instruction-tuned language model quantized to 4-bit by Unsloth. It supports up to 128K token context, multilingual input…
Qwen2.5-7B
Qwen2.5-7B is a 7.6 billion-parameter base language model from Alibaba's Qwen team, released September 2024. It supports up to 131K token context, multilingual …
Qwen2.5-7B-Instruct
Qwen2.5-7B-Instruct is a 7.6 billion parameter instruction-tuned language model from Alibaba's Qwen team. It supports up to 128K token context length with 8K ge…
Qwen2.5-7B-Instruct-AWQ
Qwen2.5-7B-Instruct-AWQ is a 7.6B-parameter instruction-tuned language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for memory efficiency. It su…
Qwen2.5-7B-Instruct-bnb-4bit
Qwen2.5-7B-Instruct-bnb-4bit is a 7.8B-parameter instruction-tuned language model from Alibaba (via Unsloth's quantized distribution) in 4-bit format. It suppor…
Qwen2.5-7B-Instruct-GGUF
Qwen2.5-7B-Instruct-GGUF is a 7.6 billion parameter instruction-tuned language model from Alibaba's Qwen team, quantized in GGUF format for efficient local depl…
Qwen2.5-7B-Instruct-GPTQ-Int4
Qwen2.5-7B-Instruct-GPTQ-Int4 is a 7.6 billion parameter instruction-tuned language model from Alibaba's Qwen team, quantized to 4-bit precision using GPTQ for …
Qwen2.5-7B-Instruct-GPTQ-Int8
Qwen2.5-7B-Instruct-GPTQ-Int8 is a 7.6 billion parameter instruction-tuned language model from Alibaba's Qwen team, quantized to 8-bit using GPTQ for reduced me…
Qwen2.5-7B-Instruct-unsloth-bnb-4bit
Qwen2.5-7B-Instruct-unsloth-bnb-4bit is a 7.8B parameter instruction-tuned language model quantized to 4-bit using bitsandbytes, optimized for inference and fin…
Qwen2.5-Coder-0.5B
Qwen2.5-Coder-0.5B is a lightweight, code-focused language model with 494M parameters designed for code generation, reasoning, and fixing tasks. Licensed under …
Qwen2.5-Coder-0.5B-Instruct
Qwen2.5-Coder-0.5B-Instruct is a 494M-parameter instruction-tuned code generation model from Alibaba's Qwen team. It is designed for code-specific tasks—writing…
Qwen2.5-Coder-1.5B
Qwen2.5-Coder-1.5B is a 1.5 billion parameter code-focused language model from Alibaba's Qwen team. It is designed for code generation, reasoning, and fixing ta…
Qwen2.5-Coder-1.5B-Instruct
Qwen2.5-Coder-1.5B-Instruct is a 1.5-billion-parameter open-source code-specialized language model from Alibaba's Qwen team. It supports a 32K token context win…
Qwen2.5-Coder-1.5B-Instruct-GGUF
Qwen2.5-Coder-1.5B-Instruct-GGUF is a 1.5 billion parameter open-source code-generation model from Alibaba's Qwen team, pre-quantized in GGUF format for efficie…
Qwen2.5-Coder-14B-Instruct
Qwen2.5-Coder-14B-Instruct is a 14.7B-parameter instruction-tuned open-source code LLM from Alibaba's Qwen team. It targets code generation, reasoning, and fixi…
Qwen2.5-Coder-14B-Instruct-AWQ
Qwen2.5-Coder-14B-Instruct-AWQ is a 14.7B parameter code-specialized language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for reduced memory fo…
Qwen2.5-Coder-14B-Instruct-bnb-4bit
Qwen2.5-Coder-14B-Instruct-bnb-4bit is a 14-billion-parameter code-focused language model quantized to 4-bit precision by Unsloth. It is optimized for code gene…
Qwen2.5-Coder-14B-Instruct-GGUF
Qwen2.5-Coder-14B-Instruct-GGUF is a 14-billion-parameter code-focused language model quantized into GGUF format for efficient local inference. It supports 128K…
Qwen2.5-Coder-14B-Instruct-GPTQ-Int8
Qwen2.5-Coder-14B-Instruct-GPTQ-Int8 is a 14.7 billion parameter code-focused language model from Alibaba's Qwen team, quantized to 8-bit using GPTQ for reduced…
Qwen2.5-Coder-14B-Instruct-MLX-4bit
Qwen2.5-Coder-14B-Instruct-MLX-4bit is a 14-billion-parameter code-focused language model quantized to 4-bit precision for Apple Silicon Macs. It is optimized f…
Qwen2.5-Coder-14B-Instruct-MLX-8bit
Qwen2.5-Coder-14B-Instruct-MLX-8bit is a 14-billion parameter code-focused large language model quantized to 8-bit precision for Apple Silicon Macs. It is a com…
Qwen2.5-Coder-32B-Instruct
Qwen2.5-Coder-32B-Instruct is a 32-billion-parameter open-source code-generation model from Alibaba's Qwen team, fine-tuned for instruction-following. It suppor…
Qwen2.5-Coder-32B-Instruct-AWQ
Qwen2.5-Coder-32B-Instruct-AWQ is a 32-billion-parameter code-focused language model from Alibaba's Qwen team, quantized to 4-bit AWQ format for reduced memory …
Qwen2.5-Coder-32B-Instruct-GGUF
Qwen2.5-Coder-32B-Instruct-GGUF is a 32.5B parameter code-specialized language model from Alibaba's Qwen team, quantized in GGUF format for efficient local depl…
Qwen2.5-Coder-32B-Instruct-GPTQ-Int4
Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 is a 32-billion-parameter open-source code-specialized language model from Alibaba's Qwen team. This variant is quantized t…
Qwen2.5-Coder-32B-Instruct-MLX-4bit
Qwen2.5-Coder-32B-Instruct-MLX-4bit is a 32 billion parameter code-focused language model quantized to 4-bit precision for Apple Silicon Macs using the MLX fram…
Qwen2.5-Coder-32B-Instruct-MLX-8bit
Qwen2.5-Coder-32B-Instruct-MLX-8bit is a 32-billion-parameter code-focused language model quantized to 8-bit precision for Apple Silicon Macs. It is derived fro…
Qwen2.5-Coder-7B
Qwen2.5-Coder-7B is a 7.6 billion parameter open-source language model optimized for code generation, code reasoning, and code fixing. It supports up to 131,072…
Qwen2.5-Coder-7B-Instruct
Qwen2.5-Coder-7B-Instruct is a 7.6B-parameter instruction-tuned code-generation model from Alibaba's Qwen team. It supports up to 131K token context, excels at …
Qwen2.5-Coder-7B-Instruct-AWQ
Qwen2.5-Coder-7B-Instruct-AWQ is a 7-billion-parameter code-focused language model quantized to 4-bit AWQ format by Alibaba Cloud's Qwen team. It is designed fo…
Qwen2.5-Coder-7B-Instruct-bnb-4bit
Qwen2.5-Coder-7B-Instruct-bnb-4bit is a 7.8B parameter code-focused language model quantized to 4-bit precision by Unsloth. It supports 131K token context, exce…
Qwen2.5-Coder-7B-Instruct-GGUF
Qwen2.5-Coder-7B-Instruct-GGUF is a 7.6 billion parameter, instruction-tuned code-focused language model from Alibaba Cloud's Qwen team, distributed in quantize…
Qwen2.5-Coder-7B-Instruct-GPTQ-Int4
Qwen2.5-Coder-7B-Instruct-GPTQ-Int4 is a 7.6B parameter code-focused language model from Alibaba's Qwen team, quantized to 4-bit for reduced memory footprint. I…
Qwen2.5-Math-1.5B
Qwen2.5-Math-1.5B is a specialized 1.5B-parameter language model optimized for solving mathematical problems in English and Chinese. It uses Chain-of-Thought (C…
Qwen2.5-Math-1.5B-Instruct
Qwen2.5-Math-1.5B-Instruct is a 1.5B parameter instruction-tuned language model optimized for solving mathematical problems in English and Chinese. It supports …
Qwen2.5-Math-7B
Qwen2.5-Math-7B is a 7.6B-parameter open-source language model fine-tuned for mathematical problem-solving in English and Chinese. It uses chain-of-thought (CoT…
Qwen2.5-Math-7B-Instruct
Qwen2.5-Math-7B-Instruct is a 7.6B-parameter instruction-tuned language model optimized for solving mathematical problems in English and Chinese. It combines Ch…
Qwen2-7B
Qwen2-7B is a 7.6-billion parameter base language model released by Alibaba's Qwen team in June 2024. It is not recommended for direct text generation without f…
Qwen2-7B-Instruct
Qwen2-7B-Instruct is a 7.6B-parameter instruction-tuned language model from Alibaba's Qwen team, released August 2024. It supports up to 131K-token context via …
Qwen2.5-72B-Instruct
Qwen2.5-72B-Instruct is a 72.7-billion-parameter instruction-tuned language model from Alibaba's Qwen team. It supports up to 131K token context length with 8K …
Qwen2.5-7B-Instruct
Qwen2.5-7B-Instruct is a 7.6B-parameter instruction-tuned language model from Alibaba Cloud's Qwen team. It supports up to 128K token context length with 8K gen…
Qwen2.5-Coder-7B-Instruct
Qwen2.5-Coder-7B-Instruct is a 7.6B-parameter instruction-tuned code-focused language model from Alibaba's Qwen team. It supports up to 131K token context lengt…
Qwen3-0.6B
Qwen3-0.6B is a 0.6B-parameter causal language model from Alibaba's Qwen team, designed for efficient deployment on resource-constrained devices. It uniquely su…
Qwen3-0.6B-8bit
Qwen3-0.6B-8bit is a lightweight, 600-million-parameter language model converted to MLX format (Apple Silicon-optimized). It is a quantized 8-bit version of Ali…
Qwen3-0.6B-Base
Qwen3-0.6B-Base is a 596M-parameter base language model from Alibaba's Qwen team, released July 2025. It is a pretrained causal LM designed for text generation …
Qwen3-0.6B-FP8
Qwen3-0.6B-FP8 is a 600M parameter lightweight language model from Alibaba's Qwen team, quantized to FP8 for efficient inference. It supports both 'thinking mod…
Qwen3-0.6B-GGUF
Qwen3-0.6B-GGUF is a 600-million parameter text-generation model from Alibaba (via Unsloth's quantized GGUF distribution). It supports switching between thinkin…
Qwen3-0.6B-unsloth-bnb-4bit
Qwen3-0.6B-unsloth-bnb-4bit is a 0.6B parameter quantized language model from Alibaba's Qwen series, optimized by Unsloth using 4-bit quantization (bitsandbytes…
Qwen3-1.7B
Qwen3-1.7B is a 1.7 billion parameter causal language model from Qwen that supports dual-mode operation: a thinking mode for complex reasoning tasks and a fast …
Qwen3-1.7B-Base
Qwen3-1.7B-Base is a 1.7 billion parameter causal language model from Alibaba's Qwen team, released in July 2025. It is a base (pretrained) model trained on 36 …
Qwen3-1.7B-FP8
Qwen3-1.7B-FP8 is a 1.7 billion parameter language model from Alibaba's Qwen team, optimized with FP8 quantization for efficient inference. It supports a unique…
Qwen3-1.7B-GPTQ-Int8
Qwen3-1.7B-GPTQ-Int8 is a 1.7 billion parameter language model from Alibaba's Qwen team, quantized to 8-bit using GPTQ for efficient deployment. It supports a u…
Qwen3-1.7B-unsloth-bnb-4bit
Qwen3-1.7B is a compact 1.7B-parameter language model from Alibaba's Qwen series, quantized to 4-bit precision by unsloth for efficient deployment. It supports …
Qwen3-14B
Qwen3-14B is a 14.8B-parameter open-source language model from Qwen that supports dynamic switching between reasoning (thinking) and standard modes within a sin…
Qwen3-14B-AWQ
Qwen3-14B-AWQ is a 14.8B parameter open-source language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for efficient deployment. It supports a uni…
Qwen3-14B-Base
Qwen3-14B-Base is a 14.8-billion-parameter open-source language model from Alibaba's Qwen team, released in July 2025. It is a base (non-instruction-tuned) mode…
Qwen3-14B-FP8
Qwen3-14B-FP8 is a 14.8 billion parameter language model from Alibaba's Qwen team, quantized to FP8 precision for reduced memory footprint. It supports a unique…
Qwen3-14B-GGUF
Qwen3-14B-GGUF is a 14.8B parameter quantized language model from Alibaba's Qwen team, designed for efficient local inference. It supports dual-mode operation (…
Qwen3-14B-GPTQ-Int4
Qwen3-14B-GPTQ-Int4 is a 4-bit quantized version of Alibaba's Qwen3-14B language model. It reduces model size and memory footprint while maintaining inference s…
Qwen3-14B-Instruct
Qwen3-14B-Instruct is a 14.8B parameter instruction-tuned language model from Alibaba's Qwen team, packaged and optimized by OpenPipe for fine-tuning workflows.…
Qwen3-14B-MLX-4bit
Qwen3-14B-MLX-4bit is a 14-billion-parameter language model quantized to 4-bit precision and optimized for Apple MLX framework. It is a community-converted vers…
Qwen3-14B-MLX-8bit
Qwen3-14B-MLX-8bit is a 14-billion parameter language model converted to Apple's MLX framework in 8-bit quantized format. It is a community conversion of the or…
Qwen3-14B-NVFP4
Qwen3-14B-NVFP4 is NVIDIA's FP4-quantized version of Alibaba's Qwen3-14B language model. It reduces model size and memory footprint through post-training quanti…
Qwen3-14B-unsloth-bnb-4bit
Qwen3-14B is a 14.8B-parameter causal language model quantized to 4-bit by Unsloth. It supports switching between thinking mode (complex reasoning) and non-thin…
Qwen3-235B-A22B
Qwen3-235B-A22B is a 235-billion-parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team. It activates only 22B parameters per inference step…
Qwen3-235B-A22B-FP8
Qwen3-235B-A22B-FP8 is a 235-billion-parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team, with only 22B parameters active per inference p…
Qwen3-235B-A22B-Instruct-2507
Qwen3-235B-A22B-Instruct-2507 is a 235-billion parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team. It activates 22B parameters per infer…
Qwen3-235B-A22B-Instruct-2507-AWQ
Qwen3-235B-A22B-Instruct-2507-AWQ is a 235-billion-parameter mixture-of-experts (MoE) language model quantized to 4-bit AWQ format by QuantTrio. It claims impro…
Qwen3-235B-A22B-Instruct-2507-FP8
Qwen3-235B-A22B-Instruct-2507-FP8 is a 235-billion-parameter mixture-of-experts (MoE) large language model from Alibaba's Qwen team. It activates only 22B param…
Qwen3-235B-A22B-Thinking-2507
Qwen3-235B-A22B-Thinking-2507 is a 235B-parameter open-source LLM from Alibaba's Qwen team, featuring a mixture-of-experts (MoE) architecture with 22B active pa…
Qwen3-235B-A22B-Thinking-2507-FP8
Qwen3-235B-A22B-Thinking-2507-FP8 is an Apache-2.0-licensed, open-source 235B-parameter mixture-of-experts language model from Alibaba's Qwen team. It is FP8-qu…
Qwen3-30B-A3B
Qwen3-30B-A3B is a 30.5B-parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team. It activates only 3.3B parameters per token, balancing reas…
Qwen3-30B-A3B-abliterated
Qwen3-30B-A3B-abliterated is a 30B parameter uncensored variant of Alibaba's Qwen3-30B-A3B model, created by applying an 'abliteration' technique to remove safe…
Qwen3-30B-A3B-Base
Qwen3-30B-A3B-Base is a 30.5B-parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team, released July 2025. It activates only 3.3B parameters …
Qwen3-30B-A3B-FP8
Qwen3-30B-A3B-FP8 is a 30.5B-parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team, with only 3.3B parameters active per token. It supports…
Qwen3-30B-A3B-FP8-Dynamic
Qwen3-30B-A3B-FP8-Dynamic is a quantized version of Alibaba's Qwen3 30B model, optimized for memory and speed. Both weights and activations are compressed to 8-…
Qwen3-30B-A3B-GPTQ-Int4
Qwen3-30B-A3B-GPTQ-Int4 is a 30.5B-parameter mixture-of-experts (MoE) model from Qwen that activates only 3.3B parameters per forward pass. It supports dual mod…
Qwen3-30B-A3B-Instruct-2507
Qwen3-30B-A3B-Instruct-2507 is a 30.5B parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team, with only 3.3B parameters activated per token…
Qwen3-30B-A3B-Instruct-2507-AWQ-4bit
Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model with only 3.3B parameters active at inference time. It is optimized for instr…
Qwen3-30B-A3B-Instruct-2507-FP8
Qwen3-30B-A3B-Instruct-2507-FP8 is a 30.5-billion parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team, with only 3.3B parameters active p…
Qwen3-30B-A3B-NVFP4
NVIDIA's quantized version of Alibaba's Qwen3-30B-A3B model, compressed to FP4 (4-bit) precision using TensorRT Model Optimizer. Reduces memory footprint by ~3.…
Qwen3-30B-A3B-quantized.w4a16
Qwen3-30B-A3B-quantized.w4a16 is a quantized version of Alibaba's Qwen3 30-billion parameter model, optimized by Red Hat (Neural Magic) using INT4 weight quanti…
Qwen3-30B-A3B-Thinking-2507
Qwen3-30B-A3B-Thinking-2507 is a 30.5B-parameter mixture-of-experts language model from Alibaba's Qwen team, with only 3.3B parameters activated per token. It f…
Qwen3-30B-A3B-Thinking-2507-AWQ-4bit
Qwen3-30B-A3B-Thinking-2507 is a 30.5B-parameter mixture-of-experts language model optimized for reasoning tasks. It uses 4-bit quantization to reduce memory fo…
Qwen3-30B-A3B-Thinking-2507-FP8
Qwen3-30B-A3B-Thinking-2507-FP8 is a 30.5B parameter open-source language model from Alibaba's Qwen team, released July 2025. It is a mixture-of-experts (MoE) m…
Qwen3-32B
Qwen3-32B is a 32.8B parameter open-source LLM from Alibaba's Qwen team that supports dynamic switching between 'thinking mode' (for reasoning-heavy tasks like …
Qwen3-32B-AWQ
Qwen3-32B-AWQ is a 32.8B-parameter quantized language model from Alibaba's Qwen team, offering switchable thinking and non-thinking modes within a single model.…
Qwen3-32B-FP8
Qwen3-32B-FP8 is a 32.8B-parameter open-source language model from Alibaba's Qwen team, available in FP8 (8-bit floating-point) quantized form. It supports both…
Qwen3-32B-NVFP4
NVIDIA's Qwen3-32B-FP4 is a quantized 32.8B-parameter language model based on Alibaba's Qwen3-32B, optimized for inference on NVIDIA GPUs using TensorRT-LLM. It…
Qwen3-4B
Qwen3-4B is a 4-billion-parameter open-source LLM from Alibaba's Qwen team. It supports dynamic switching between 'thinking mode' (for reasoning-heavy tasks lik…
Qwen3-4B-AWQ
Qwen3-4B-AWQ is a 4-billion parameter language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for efficient inference. It supports dynamic switchi…
Qwen3-4B-Base
Qwen3-4B-Base is a 4-billion-parameter base language model from Qwen, designed for text generation and conversational tasks. It is pre-trained on 36 trillion to…
Qwen3-4B-DFlash-b16
Qwen3-4B-DFlash-b16 is a lightweight diffusion-based draft model designed to accelerate inference of Qwen3-4B through speculative decoding. It does not function…
Qwen3-4B-FP8
Qwen3-4B-FP8 is a 4-billion-parameter instruction-tuned language model from Alibaba's Qwen team, quantized to FP8 precision for efficient inference. It supports…
Qwen3-4B-GGUF
Qwen3-4B-GGUF is a quantized 4-billion-parameter language model from Alibaba's Qwen team that supports both 'thinking mode' (for complex reasoning) and 'non-thi…
Qwen3-4B-Instruct-2507
Qwen3-4B-Instruct-2507 is a 4-billion-parameter instruction-tuned language model from Alibaba's Qwen team. It supports 262K context natively and is optimized fo…
Qwen3-4B-Instruct-2507-4bit
Qwen3-4B-Instruct-2507-4bit is a 4-bit quantized version of Alibaba's 4B parameter instruction-tuned language model, converted to MLX format for efficient infer…
Qwen3-4B-Instruct-2507-AWQ-4bit
Qwen3-4B-Instruct-2507-AWQ-4bit is a quantized version of Alibaba's 4-billion-parameter instruction-tuned language model. It uses 4-bit AWQ (Activation-aware We…
Qwen3-4B-Instruct-2507-FP8
Qwen3-4B-Instruct-2507-FP8 is a 4-billion-parameter instruction-tuned language model from Alibaba's Qwen team, released in quantized FP8 format for reduced memo…
Qwen3-4B-Instruct-2507-MLX-4bit
Qwen3-4B-Instruct-2507-MLX-4bit is a 4-bit quantized version of Alibaba's Qwen3 4B instruction-tuned language model, optimized for Apple Silicon using MLX. It i…
Qwen3-4B-Instruct-2507-MLX-5bit
Qwen3-4B-Instruct-2507-MLX-5bit is a 4B-parameter instruction-tuned language model quantized to 5-bit precision using Apple's MLX framework. It is optimized for…
Qwen3-4B-Instruct-2507-MLX-6bit
Qwen3-4B-Instruct-2507-MLX-6bit is a 4-billion-parameter instruction-tuned language model quantized to 6-bit precision using MLX framework. It is optimized for …
Qwen3-4B-Instruct-2507-MLX-8bit
Qwen3-4B-Instruct-2507-MLX-8bit is a 4-billion-parameter instruction-tuned language model quantized to 8-bit precision using MLX, optimized for Apple Silicon de…
Qwen3-4B-Instruct-2507-NVFP4
Qwen3-4B-Instruct-2507-NVFP4 is a 2.8B parameter instruction-tuned language model quantized to NVFP4 format. It is a compressed version of Qwen's base model, de…
Qwen3-4B-Instruct-2507-unsloth-bnb-4bit
Qwen3-4B-Instruct-2507 is a 4 billion parameter quantized language model from Alibaba's Qwen team, optimized by Unsloth using 4-bit quantization. It supports 25…
Qwen3-4B-MLX-4bit
Qwen3-4B-MLX-4bit is a compact 4B-parameter language model from Alibaba's Qwen team, optimized for Apple Silicon via MLX and quantized to 4-bit precision. It su…
Qwen3-4B-Thinking-2507
Qwen3-4B-Thinking-2507 is a 4-billion-parameter open-source language model from Alibaba's Qwen team, released August 2025. It features extended reasoning capabi…
Qwen3-4B-Thinking-2507-FP8
Qwen3-4B-Thinking-2507-FP8 is a 4-billion-parameter language model from Alibaba's Qwen team, optimized for reasoning tasks via internal 'thinking' capability. T…
Qwen3-4B-Thinking-2507-MLX-4bit
Qwen3-4B-Thinking-2507-MLX-4bit is a 4-bit quantized version of Qwen's 4B parameter language model, optimized for Apple Silicon using MLX. It is a community-pro…
Qwen3-4B-Thinking-2507-MLX-6bit
Qwen3-4B-Thinking-2507-MLX-6bit is a 4-billion-parameter quantized language model optimized for Apple Silicon via MLX. It is a 6-bit quantized version of Qwen's…
Qwen3-4B-Thinking-2507-MLX-8bit
Qwen3-4B-Thinking-2507-MLX-8bit is a 1.1B-parameter language model quantized to 8-bit precision and optimized for Apple Silicon via MLX. It is derived from Qwen…
Qwen3-4B-unsloth-bnb-4bit
Qwen3-4B is a 4 billion parameter quantized language model from Alibaba's Qwen series, optimized by Unsloth using 4-bit quantization (bitsandbytes). It supports…
Qwen3.5-122B-A10B-heretic-MTP-NVFP4
Qwen3.5-122B-A10B-heretic-MTP-NVFP4 is a quantized (W4A4) variant of Qwen's 122B mixture-of-experts model with abliteration modifications. It uses NVFP4 quantiz…
Qwen3.5-122B-A10B-NVFP4
Qwen3.5-122B-A10B-NVFP4 is a quantized 122B parameter language model (with 10B active parameters via mixture-of-experts) derived from Alibaba's Qwen3.5-122B-A10…
Qwen3.5-27B-DFlash
Qwen3.5-27B-DFlash is a specialized draft model (not a standalone LLM) designed to speed up inference of the Qwen3.5-27B base model using DFlash speculative dec…
Qwen3.5-27B-OptiQ-4bit
Qwen3.5-27B-OptiQ-4bit is a 27-billion-parameter language model quantized to 4-bit precision with selective 8-bit layers for sensitive components. It runs on Ap…
Qwen3.5-397B-A17B-NVFP4
This is NVIDIA's quantized version of Alibaba's Qwen3.5-397B-A17B, a 397-billion-parameter mixture-of-experts language model compressed to FP4 precision for eff…
Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled is a 4-billion-parameter language model fine-tuned to excel at step-by-step reasoning tasks. It distills reasonin…
Qwen3.5-9B-GLM5.1-Distill-v1-GGUF
Qwen3.5-9B-GLM5.1-Distill-v1 is a 9-billion-parameter language model fine-tuned via knowledge distillation from GLM-5.1 reasoning data. It emphasizes structured…
Qwen3.6-12B-IQ-Ultra-Heretic-Uncensored-Thinking-V2-Hightop-GGUF
Qwen3.6-12B-IQ-Ultra-Heretic-Uncensored-Thinking-V2-Hightop-GGUF is a quantized 12-billion-parameter text generation model distributed as GGUF (GPU UQIF Format)…
Qwen3.6-14B-A3B-FableVibes-GGUF
Qwen3.6-14B-A3B-FableVibes-GGUF is a 14B-parameter mixture-of-experts language model quantized for CPU/GPU inference via llama.cpp. It was created by pruning a …
Qwen3.6-14B-A3B-VibeForged-v2-GGUF
Qwen3.6-14B-A3B-VibeForged-v2-GGUF is a 14B parameter quantized language model optimized for local deployment via llama.cpp. It includes vision capabilities and…
Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS
Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS is a 27B parameter multimodal LLM quantized to NVFP4 precision with multi-token prediction (MTP) ca…
Qwen3.6-27B-DFlash
Qwen3.6-27B-DFlash is a 27-billion-parameter drafter model designed specifically for speculative decoding with the Qwen3.6-27B target model. It uses a novel 'bl…
Qwen3.6-27B-GGUF
Qwen 3.6-27B is a dense 27-billion-parameter language model from Alibaba, quantized to GGUF format by BatiAI for efficient on-device inference on Apple Silicon …
Qwen3.6-27B-MTP-pi-tune-GGUF
Qwen3.6-27B-MTP-pi-tune-GGUF is a 27-billion-parameter language model fine-tuned for fast, agent-friendly task execution without internal reasoning blocks. It u…
Qwen3.6-27B-NVFP4
Qwen3.6-27B-NVFP4 is a 27-billion-parameter quantized language model from NVIDIA, derived from Alibaba's Qwen3.6-27B. It compresses the original model to 4-bit …
Qwen3.6-27B-Text-NVFP4-MTP
Qwen3.6-27B-Text-NVFP4-MTP is a quantized text-only variant of Alibaba's Qwen3.6-27B base model. It uses NVIDIA's NVFP4 quantization format (modelopt native) an…
Qwen3.6-35B-A3B-Abliterated-Heretic-AWQ-4bit
Qwen3.6-35B-A3B-Abliterated-Heretic-AWQ-4bit is a 36.2B-parameter multimodal Mixture-of-Experts model quantized to 4-bit for efficient inference. It handles tex…
Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
A 35B parameter reasoning-focused LLM fine-tuned via supervised learning on Claude Opus chain-of-thought data. Distributed as GGUF quantizations for local infer…
Qwen3.6-35B-A3B-DFlash
Qwen3.6-35B-A3B-DFlash is a specialized 35B-parameter draft model designed for speculative decoding—it is not a standalone LLM. It works alongside Qwen3.6-35B-A…
Qwen3.6-35B-A3B-NVFP4
NVIDIA's Qwen3.6-35B-A3B-NVFP4 is a quantized version of Alibaba's Qwen3.6 language model, optimized for inference on NVIDIA GPUs. It uses 4-bit quantization (N…
Qwen3.6-35B-A3B-PRISM-NVFP4
Qwen3.6-35B-A3B-PRISM-NVFP4 is a quantized 35-billion-parameter mixture-of-experts language model from Ex0bit, optimized for NVIDIA Blackwell GPUs. It combines …
Qwen3-8B
Qwen3-8B is an 8.2-billion-parameter open-source language model from Alibaba's Qwen team. It supports a unique 'thinking mode' for complex reasoning (math, codi…
Qwen3-8B-AWQ
Qwen3-8B-AWQ is an 8.2-billion-parameter language model from Alibaba's Qwen team, quantized to 4-bit using AWQ for efficient inference. It uniquely supports tog…
Qwen3-8B-Base
Qwen3-8B-Base is an 8.2-billion-parameter base language model from Alibaba's Qwen team, released in May 2025. It is a pre-trained causal language model trained …
Qwen3-8B-DFlash-b16
Qwen3-8B-DFlash-b16 is a lightweight drafting model designed to accelerate inference of the Qwen3-8B target model using speculative decoding. It uses block diff…
Qwen3-8B-FP8
Qwen3-8B-FP8 is an 8.2B-parameter open-source language model from Alibaba's Qwen team. It offers dual modes: a 'thinking' mode for complex reasoning (math, code…
Qwen3-8B-FP8-dynamic
Qwen3-8B-FP8-dynamic is an 8-billion-parameter language model quantized to FP8 precision by Red Hat AI. It reduces memory footprint by ~50% and increases throug…
Qwen3-8B-GGUF
Qwen3-8B-GGUF is a quantized version of Alibaba's latest 8-billion-parameter language model, optimized for CPU/low-resource inference via GGUF format. It suppor…
Qwen3-8B-NVFP4
Qwen3-8B-NVFP4 is NVIDIA's quantized version of Alibaba's Qwen3-8B language model, compressed to FP4 precision for efficient inference. It is optimized for depl…
Qwen3-8B-quantized.w4a16
Qwen3-8B-quantized.w4a16 is an 8.3B-parameter language model compressed to INT4 weights, reducing memory and disk by ~75% while maintaining near-baseline accura…
Qwen3-8B-speculator.eagle3
Qwen3-8B-speculator.eagle3 is a specialized acceleration model for the Qwen3-8B LLM using EAGLE-3 speculative decoding. It predicts multiple future tokens in pa…
Qwen3-8B.w8a8
Qwen3-8B.w8a8 is an 8-bit quantized version of Qwen3-8B optimized for NVIDIA Ampere GPUs. It reduces model size and memory footprint through INT8 quantization w…
Qwen3-Coder-30B-A3B-Instruct
Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter open-source coding LLM from Alibaba's Qwen team, released December 2025. It uses a mixture-of-experts (MoE) ar…
Qwen3-Coder-30B-A3B-Instruct-AWQ
Qwen3-Coder-30B-A3B-Instruct-AWQ is a 4-bit quantized version of a 30.5B parameter mixture-of-experts coding model from Alibaba's Qwen team. It supports 262K to…
Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit
Qwen3-Coder-30B-A3B-Instruct is a 30.5B-parameter open-source coding LLM with a Mixture-of-Experts (MoE) architecture that activates only 3.3B parameters at inf…
Qwen3-Coder-30B-A3B-Instruct-FP8
Qwen3-Coder-30B-A3B-Instruct-FP8 is a 30.5-billion-parameter mixture-of-experts coding LLM from Qwen (Alibaba), optimized for code generation, agentic coding ta…
Qwen3-Coder-30B-A3B-Instruct-GGUF
Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter mixture-of-experts (MoE) coding model with 3.3B activated parameters, distributed as a GGUF quantization by Un…
Qwen3-Coder-30B-A3B-Instruct-MLX-4bit
Qwen3-Coder-30B-A3B-Instruct-MLX-4bit is a 30B-parameter code-focused language model quantized to 4-bit precision for Apple Silicon devices. It is a community-m…
Qwen3-Coder-30B-A3B-Instruct-MLX-5bit
Qwen3-Coder-30B-A3B-Instruct-MLX-5bit is a 30-billion-parameter code-focused language model quantized to 5-bit precision for Apple Silicon devices. It is a comm…
Qwen3-Coder-30B-A3B-Instruct-MLX-6bit
Qwen3-Coder-30B-A3B-Instruct-MLX-6bit is a 30B-parameter code-focused language model quantized to 6-bit precision and optimized for Apple Silicon using MLX. It …
Qwen3-Coder-30B-A3B-Instruct-MLX-8bit
Qwen3-Coder-30B-A3B-Instruct-MLX-8bit is an 8-bit quantized code-generation model optimized for Apple Silicon, based on Qwen's 30B parameter coder. It is distri…
Qwen3-Coder-480B-A35B-Instruct-FP8
Qwen3-Coder-480B-A35B-Instruct-FP8 is a 480-billion-parameter mixture-of-experts (MoE) code generation model from Alibaba's Qwen team, with 35B parameters activ…
Qwen3-Coder-Next
Qwen3-Coder-Next is a 80B-parameter open-weight language model optimized for coding tasks and agentic workflows. It uses a mixture-of-experts architecture with …
Qwen3-Coder-Next-AWQ-4bit
Qwen3-Coder-Next-AWQ-4bit is a 4-bit quantized version of Qwen's 80B-parameter mixture-of-experts coding model, with only 3B parameters activated per inference.…
Qwen3-Coder-Next-AWQ-8bit
Qwen3-Coder-Next is an open-weight 80B-parameter language model optimized for code generation and agentic tasks. It uses a mixture-of-experts (MoE) architecture…
Qwen3-Coder-Next-FP8
Qwen3-Coder-Next-FP8 is an open-weight, 80-billion-parameter language model optimized for coding tasks and agentic workflows. Despite its large parameter count,…
Qwen3-Coder-Next-GGUF
Qwen3-Coder-Next is an open-weight coding LLM with 80B total parameters but only 3B activated, optimized for local deployment and agentic coding tasks. It suppo…
Qwen3-Coder-Next-NVFP4
Qwen3-Coder-Next-NVFP4 is an NVIDIA FP4-quantized version of Qwen's 80B parameter mixture-of-experts code generation model. It compresses the model from ~149GB …
Qwen3-Embedding-0.6B
Qwen3-Embedding-0.6B is a lightweight (595M parameters) text embedding model from Alibaba's Qwen team, designed for semantic search, classification, and cluster…
Qwen3-Embedding-4B
Qwen3-Embedding-4B is a 4-billion-parameter text embedding model from Alibaba's Qwen team, optimized for semantic search, ranking, and document retrieval tasks.…
Qwen3-Embedding-4B-AWQ-INT4
Qwen3-Embedding-4B-AWQ-INT4 is a compressed 4B-parameter embedding model quantized to INT4 format, optimized to run on consumer GPUs with ~2.7 GB disk footprint…
Qwen3-Embedding-4B-W4A16-G128
Qwen3-Embedding-4B-W4A16-G128 is a quantized 4-billion-parameter embedding model optimized for text similarity, clustering, and retrieval tasks. It reduces memo…
Qwen3-Embedding-8B-AWQ-INT4
Qwen3-Embedding-8B-AWQ-INT4 is a quantized (4-bit INT4) version of Qwen's 8-billion-parameter embedding model, optimized for text generation. It runs on consume…
Qwen3-Next-80B-A3B-Instruct
Qwen3-Next-80B-A3B-Instruct is an 80-billion parameter language model with a sparse Mixture-of-Experts architecture that activates only 3 billion parameters per…
Qwen3-Next-80B-A3B-Instruct-AWQ-4bit
Qwen3-Next-80B-A3B-Instruct is a 80-billion parameter language model from Alibaba with only 3 billion parameters active per token, using a mixture-of-experts ar…
Qwen3-Next-80B-A3B-Instruct-FP8
Qwen3-Next-80B-A3B-Instruct-FP8 is an 80-billion-parameter open-source language model from Alibaba's Qwen team, optimized for instruction-following tasks. It us…
Qwen3-Next-80B-A3B-Instruct-NVFP4
NVIDIA's Qwen3-Next-80B-A3B-Instruct-NVFP4 is a quantized 80-billion-parameter large language model optimized for inference on NVIDIA GPUs. It reduces model siz…
Qwen3-Next-80B-A3B-Thinking-AWQ-4bit
Qwen3-Next-80B-A3B-Thinking is a 80-billion-parameter open-source reasoning model from Alibaba's Qwen team. It uses sparse mixture-of-experts and hybrid attenti…
Qwen3-Reranker-0.6B
Qwen3-Reranker-0.6B is a lightweight text reranking model (595M parameters) from Alibaba's Qwen team, designed to score and reorder candidate documents for rele…
Qwen3-Reranker-4B
Qwen3-Reranker-4B is a 4-billion-parameter text reranking model from Alibaba's Qwen team, designed to score and rank candidate documents against queries. It sup…
Qwen3-Reranker-4B-W4A16-G128
Qwen3-Reranker-4B-W4A16-G128 is a quantized (4-bit weight, 16-bit activation) reranking model derived from Qwen's 4B base. It reduces VRAM usage from ~17.4GB to…
Qwen3-Reranker-8B
Qwen3-Reranker-8B is an 8-billion parameter text reranking model from Alibaba's Qwen team, designed to score and rank query-document pairs. It supports 100+ lan…
Qwen3-VL-30B-A3B-Instruct-AWQ
Qwen3-VL-30B-A3B-Instruct-AWQ is a 4-bit quantized vision-language model from QuantTrio, based on Alibaba's Qwen3-VL-30B-A3B-Instruct. It supports multimodal in…
Qwen3-VL-30B-A3B-Thinking-AWQ
Qwen3-VL-30B-A3B-Thinking-AWQ is a 31B-parameter quantized vision-language model from Alibaba's Qwen team. It combines visual understanding with text reasoning,…
Qwen3Guard-Gen-0.6B
Qwen3Guard-Gen-0.6B is a 751M-parameter safety classification model from Qwen that categorizes text (prompts or model responses) into three severity levels: Saf…
Qwen3Guard-Gen-8B
Qwen3Guard-Gen-8B is an 8-billion-parameter safety moderation model designed to classify user prompts and AI responses into Safe, Controversial, or Unsafe categ…
Qwopus3.6-27B-Coder-MTP-GGUF
Qwopus3.6-27B-Coder is a 27-billion-parameter open-source language model fine-tuned for coding and agent tasks. It builds on Qwopus3.6-27B-v2 (itself a reasonin…
QwQ-32B
QwQ-32B is a 32.5-billion-parameter reasoning model from Qwen designed to solve complex problems through chain-of-thought reasoning. It supports up to 131,072 t…
Qwythos-9B-Claude-Mythos-5-1M
Qwythos-9B is a 9.4B-parameter reasoning model fine-tuned on Qwen3.5-9B with 500M+ tokens of Claude-style reasoning traces. It offers a 1M-token context window …
RaDialog-interactive-radiology-report-generation
RaDialog is a specialized vision-language model designed to generate radiology reports from chest X-ray images and engage in conversational assistance. It combi…
rank1-7b
rank1-7b is a 7-billion-parameter reranking model built on Qwen2.5-7B that improves document relevance scoring for information retrieval. Before deciding if a d…
gpt-oss-120b
gpt-oss-120b is a 120-billion-parameter open-weight language model from OpenAI, released under Apache 2.0 license. It uses mixture-of-experts (MoE) architecture…
Qwen3-30B-A3B-NVFP4
Qwen3-30B-A3B-NVFP4 is a quantized version of Qwen's 30-billion-parameter mixture-of-experts model, compressed to FP4 precision for weights and activations. It …
Qwen3-Coder-Next-NVFP4
Qwen3-Coder-Next-NVFP4 is a quantized code generation model from Red Hat, optimized for deployment with 75% smaller memory footprint than the base model. It mai…
Ring-2.5-1T
Ring-2.5-1T is a 1-trillion-parameter open-source language model developed by inclusionAI, designed for reasoning-heavy and agentic tasks. It uses a hybrid line…
Rio-3.0-Open-Mini
Rio 3.0 Open Mini is a 4B-parameter reasoning model developed by IplanRIO (Rio de Janeiro's municipal IT company) and based on Qwen3-4B. It uses distillation fr…
rnj-1
Rnj-1 is an 8.3B parameter open-weight language model developed by EssentialAI, trained from scratch with a focus on code generation, mathematics, and STEM task…
sarvam-105b
Sarvam-105B is a 106B-parameter open-source Mixture-of-Experts (MoE) model with 10.3B active parameters per token, designed for complex reasoning, coding, and m…
sarvam-30b
Sarvam-30B is a 30-billion-parameter open-source Mixture-of-Experts (MoE) language model optimized for multilingual support across 22 Indian languages and effic…
sarvam-30b-FP8-dynamic
sarvam-30b-FP8-dynamic is a quantized 30B parameter multilingual LLM optimized for inference. RedHatAI applied FP8 quantization to the original sarvamai/sarvam-…
Seed-OSS-36B-Instruct
Seed-OSS-36B-Instruct is a 36-billion-parameter open-source language model from ByteDance's Seed Team, released under Apache-2.0. It supports 512K context lengt…
Seed-OSS-36B-Instruct-MLX-8bit
Seed-OSS-36B-Instruct-MLX-8bit is a 36 billion parameter instruction-tuned language model quantized to 8-bit precision and optimized for Apple Silicon using MLX…
SmolLM-1.7B
SmolLM-1.7B is a compact language model with 1.7 billion parameters trained on a curated dataset of high-quality educational and synthetic content. It runs on m…
SmolLM-1.7B-Instruct-quantized.w4a16
SmolLM-1.7B-Instruct-quantized.w4a16 is a 1.7B parameter instruction-tuned language model compressed to INT4 weights, reducing memory footprint by ~75% while ma…
SmolLM-135M
SmolLM-135M is a lightweight text-generation model with 135M parameters developed by HuggingFace, designed for CPU and edge deployment. It trains on high-qualit…
SmolLM2-1.7B
SmolLM2-1.7B is a lightweight, open-source language model with 1.7 billion parameters designed to run on consumer hardware while maintaining competitive perform…
SmolLM2-1.7B-Instruct
SmolLM2-1.7B-Instruct is a compact, instruction-tuned language model with 1.7 billion parameters designed to run on modest hardware while handling text generati…
SmolLM2-1.7B-Instruct-GGUF
SmolLM2-1.7B-Instruct-GGUF is a quantized version of HuggingFace's 1.7B-parameter instruction-tuned language model, optimized for CPU/edge inference via llama.c…
SmolLM2-135M
SmolLM2-135M is a 135-million-parameter language model from HuggingFace designed for on-device deployment. It trades raw capability for extreme portability (~72…
SmolLM2-135M-Instruct
SmolLM2-135M-Instruct is a 135-million-parameter instruction-tuned language model from HuggingFace designed for on-device and edge deployment. It handles text g…
SmolLM2-360M
SmolLM2-360M is a 361M-parameter open-source language model from HuggingFace designed for on-device inference. It trades raw capability for efficiency, suitable…
SmolLM2-360M-Instruct
SmolLM2-360M-Instruct is a 361M-parameter open-source language model designed for on-device deployment. It is trained on 4 trillion tokens and fine-tuned for in…
SmolLM3-3B
SmolLM3-3B is a 3-billion parameter open-source language model designed for efficient inference on resource-constrained hardware. It supports dual-mode reasonin…
SmolLM3-3B-Base
SmolLM3-3B-Base is a 3 billion parameter language model from HuggingFace optimized for efficiency without sacrificing reasoning capability. It supports 6 langua…
snowflake-arctic-instruct
Snowflake Arctic is a 480B-parameter hybrid dense-MoE transformer released under Apache-2.0 by Snowflake's AI Research Team. It uses a 10B dense core with 128 e…
solar-pro-preview-instruct
Solar Pro Preview is a 22-billion-parameter instruction-tuned language model from Upstage designed to run on a single 80GB GPU. It is a pre-release version with…
stablelm-3b-4e1t
StableLM-3B-4E1T is a 2.8B parameter, open-source language model from Stability AI trained on 1 trillion tokens across diverse English and code datasets. It use…
Qwen3-Coder-30B-A3B-Instruct-AWQ
Qwen3-Coder-30B-A3B-Instruct is a 30.5B-parameter mixture-of-experts coding model with 3.3B active parameters, quantized to INT4 by stelterlab using llm-compres…
Step-3.5-Flash
Step 3.5 Flash is a 196B parameter sparse Mixture-of-Experts (MoE) foundation model from StepFun that activates ~11B parameters per token. It targets reasoning,…
Step-3.7-Flash
Step-3.7-Flash is a 198B-parameter sparse Mixture-of-Experts vision-language model that activates ~11B parameters per token, delivering up to 400 tokens/second …
Step-3.7-Flash-NVFP4
Step-3.7-Flash-NVFP4 is a 198B-parameter sparse mixture-of-experts vision-language model from StepFun that activates ~11B parameters per token. It processes ima…
step3
Step3 is a 321B-parameter multimodal model (38B active via Mixture-of-Experts) from StepFun that processes both images and text. It uses custom attention mechan…
stories15M_MOE
stories15M_MOE is a 36M-parameter mixture-of-experts (MoE) model built by repeating a 15M TinyLLaMA variant four times as separate experts. It is explicitly lab…
sundial-base-128m
Sundial is a 128M-parameter generative time-series foundation model pre-trained on 1 trillion time points. It performs zero-shot point and probabilistic forecas…
svara-tts-v1
svara-TTS v1 is an open-source multilingual text-to-speech model supporting 19 languages (18 Indic languages plus Indian English). Built by Kenpath Technologies…
t5-3b
T5-3B is a 2.85-billion-parameter text-to-text transformer developed by Google, trained on C4 and multiple supervised NLP datasets. It handles translation, summ…
tiny-random-gpt-oss-mxfp4
tiny-random-gpt-oss-mxfp4 is a 6.9B parameter GPT-style text generation model released by Intel's Optimum team, quantized in MXFP4 format for efficient inferenc…
tiny-random-Llama-3
tiny-random-Llama-3 is a minimal Llama 3 variant with ~4.1M parameters, designed for testing and development rather than production inference. It is Apache 2.0 …
tiny-random-PhiForCausalLM
tiny-random-PhiForCausalLM is a minimal 80K-parameter causal language model based on the Phi architecture, published by echarlaix on HuggingFace. It is not a pr…
tiny-random-qwen3
tiny-random-qwen3 is a minimal debugging version of Qwen3-4B-Instruct, containing ~2.4M parameters instead of the full 4B model. It is licensed under Apache 2.0…
TinyLlama-1.1B-Chat-v0.3-AWQ
TinyLlama-1.1B-Chat-v0.3-AWQ is a 1.1 billion parameter language model quantized to 4-bit using the AWQ method. It is optimized for inference efficiency on smal…
TinyLlama-1.1B-Chat-v0.3-GPTQ
TinyLlama-1.1B-Chat-v0.3-GPTQ is a quantized 1.1B parameter language model optimized for GPU inference. TheBloke provides multiple GPTQ quantization variants (4…
TinyLlama-1.1B-Chat-v1.0
TinyLlama-1.1B-Chat is a 1.1 billion parameter conversational LLM pretrained on 3 trillion tokens, designed for resource-constrained environments. It uses the L…
TinyLlama-1.1B-Chat-v1.0-GPTQ
TinyLlama-1.1B-Chat-v1.0-GPTQ is a quantized 1.1B parameter conversational LLM optimized for resource-constrained inference. TheBloke provides multiple GPTQ qua…
TinyLlama-1.1B-intermediate-step-1431k-3T
TinyLlama-1.1B is a 1.1 billion parameter language model trained on 3 trillion tokens, designed to match Llama 2's architecture while fitting into memory-constr…
tinyllama-bnb-4bit
TinyLlama 1.1B 4-bit quantized model from Unsloth, optimized for fast inference and fine-tuning on resource-constrained hardware. The model is pretrained, quant…
TinyLLama-v0
TinyLLama-v0 is a 4.6M-parameter language model built on Llama architecture, trained on the TinyStories dataset. It is a proof-of-concept release designed for g…
TinyLlama_v1.1
TinyLlama v1.1 is a 1.1B-parameter language model built on Llama 2 architecture, trained on 2 trillion tokens. It is designed for memory-constrained environment…
Tongyi-DeepResearch-30B-A3B
Tongyi-DeepResearch-30B is a 30-billion-parameter mixture-of-experts model from Alibaba that activates only 3B parameters per token. It is purpose-built for age…
Trinity-Mini-GGUF
Trinity-Mini-GGUF is a quantized version of Arcee AI's 26B parameter mixture-of-experts model (3B active parameters) optimized for local inference via llama.cpp…
typhoon2.5-qwen3-4b
Typhoon2.5-Qwen3-4B is a 4-billion-parameter Thai/English language model maintained by Typhoon AI. It offers 256K context length, function-calling support, and …
DeepSeek-R1-0528-Qwen3-8B-GGUF
DeepSeek-R1-0528-Qwen3-8B-GGUF is a quantized 8B parameter language model created by Unsloth, distilled from DeepSeek's larger reasoning model. It is optimized …
GLM-4.7-Flash
GLM-4.7-Flash is a 30B mixture-of-experts language model optimized for efficient deployment while maintaining competitive performance on reasoning and coding ta…
Qwen2.5-1.5B-Instruct
Qwen2.5-1.5B-Instruct is a 1.5 billion parameter instruction-tuned language model from Alibaba's Qwen team, distributed via Unsloth. It is designed for efficien…
Qwen2.5-14B-Instruct
Qwen2.5-14B-Instruct is a 14.7B-parameter instruction-tuned language model from Alibaba via Unsloth, supporting 29+ languages and up to 128K token context. It i…
Qwen2.5-7B-Instruct
Qwen2.5-7B-Instruct is a 7.6B-parameter instruction-tuned language model from Alibaba's Qwen team, distributed via Unsloth. It supports 29+ languages, handles u…
Qwen3-4B-GGUF
Qwen3-4B-GGUF is a 4-billion parameter language model from Alibaba (distributed via Unsloth) available in GGUF quantized format. It supports switching between '…
Qwen3-8B
Qwen3-8B is an 8.2B-parameter open-source language model from Alibaba's Qwen series that supports both thinking (reasoning) and non-thinking (fast) modes within…
Qwen3-Embedding-4B
Qwen3-Embedding-4B is a 4-billion-parameter text embedding model from Alibaba's Qwen team (served via unsloth). It converts text into dense vectors for retrieva…
VertaLily-1.2-1B-GGUF
VertaLily-1.2-1B is a compact 1 billion parameter language model optimized for local, offline deployment on resource-constrained devices (mobile, edge, CPU). It…
VibeThinker-3B
VibeThinker-3B is a 3-billion-parameter open-source LLM from WeiboAI optimized for verifiable reasoning tasks like mathematics, competitive coding, and STEM pro…
VibeThinker-3B-GGUF
VibeThinker-3B is a 3-billion-parameter language model optimized for reasoning tasks—mathematics, coding, and STEM. It is offered in GGUF format (quantized for …
VibeThinker-3B-Q4_K_M-GGUF
VibeThinker-3B-Q4_K_M-GGUF is a 3-billion-parameter language model quantized to GGUF format for efficient local deployment. It is optimized for running on consu…
VibeVoice-1.5B
VibeVoice-1.5B is Microsoft's open-source text-to-speech model designed to generate long-form, multi-speaker conversational audio (e.g., podcasts) from text. It…
VLM2Vec-Full
VLM2Vec-Full is a 4.1B-parameter multimodal embedding model built on Microsoft's Phi-3.5-vision backbone. It converts a vision-language model into an embedding …
voyage-4-nano
voyage-4-nano is a lightweight text embedding model (346M parameters) from Voyage AI designed for semantic search and retrieval. It supports multilingual conten…
wildguard
WildGuard is a 7.2B parameter safety classifier built on Mistral, designed to detect and moderate harmful content in text. Developed by AllenAI, it's gated on H…
WizardLM-2-7B-GGUF
WizardLM-2-7B-GGUF is a quantized version of Microsoft's 7-billion parameter language model, optimized for CPU/local inference via the GGUF format. It is based …
xglm-564M
XGLM-564M is a 564-million-parameter multilingual open-source language model from Meta, trained on balanced data across 30 languages including English, Chinese,…
xlnet-base-cased
XLNet (base-cased) is a 2019 pre-trained transformer language model designed for understanding tasks rather than text generation. It uses a permutation language…
Yi-1.5-6B-Chat-GGUF
Yi-1.5-6B-Chat-GGUF is a 6-billion-parameter conversational LLM quantized into GGUF format for efficient local inference. It is a community-optimized distributi…
Zamba2-1.2B-instruct
Zamba2-1.2B-Instruct is a 1.2B parameter instruction-tuned LLM combining Mamba2 state-space layers with shared transformer attention blocks. It is licensed unde…
zephyr-7b-beta
Zephyr-7B-β is a 7-billion-parameter open-source chat model fine-tuned from Mistral-7B using Direct Preference Optimization (DPO). It ranks highly on standard b…
zephyr-7b-beta
Zephyr-7B-β is a 7B-parameter open-source chat model fine-tuned from Mistral-7B-v0.1 using Direct Preference Optimization (DPO). It ranks among the highest-perf…
A software development agency for open-source projects
DEV.co is a software development agency offering custom software development services and web development for companies adopting open-source llm models. Our software developers and web developers evaluate, implement, integrate, customize, and maintain open-source software in production — so your team ships faster with less risk.
Building with open-source llm models?
DEV.co helps companies evaluate, customize, integrate, and deploy open-source software with senior software developers.