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 base model. It has been quantized to GGUF format for efficient local deployment. The model supports up to 128k token context and reports strong reasoning benchmarks (SOTA AIME24 for open models). It is provided under MIT license and gated=false, making it freely accessible.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | lmstudio-community |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 84.6k |
| Likes | 52 |
| Last updated | 2025-05-29 |
| Source | lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF |
What DeepSeek-R1-0528-Qwen3-8B-GGUF is
A distilled reasoning model combining DeepSeek-R1's chain-of-thought training with Qwen3-8B as the base. Quantized via llama.cpp (release b5524) by bartowski. Supports 128k context length. Parameter count, exact quantization precision, and training details are not provided in the model card. Last updated 2025-05-29.
Run DeepSeek-R1-0528-Qwen3-8B-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE (unverified): 8B parameters in GGUF format typically require 4–8 GB VRAM for GPU inference, or 8–16 GB RAM for CPU inference, depending on quantization bit-depth (not specified in card). For 128k context, memory footprint scales linearly; typical batch size 1 inference ~6–12 GB. Verify exact quantization level and test on target hardware.
Not explicitly documented. LoRA/QLoRA fine-tuning is plausible on standard 24GB+ VRAM hardware, but the model card provides no guidance on training recipes, data format, or optimization. Requires experimental validation or community discussion.
When to avoid it — and what to weigh
- Need guaranteed production reliability and support — This is a community-provided model. LM Studio explicitly disclaims responsibility for accuracy, completeness, security, uptime, or fitness for purpose. No vendor SLA or support channel is evident.
- Require state-of-the-art performance at scale — At 8B parameters, this model will underperform larger proprietary systems on complex reasoning. The distillation may introduce quality loss compared to the original DeepSeek-R1.
- Handling sensitive or safety-critical applications — Model card notes the model can produce harmful, offensive, inaccurate, or deceptive content. No safety fine-tuning or content filtering details are disclosed. Not suitable without additional safeguards.
- Require detailed model architecture or training transparency — Parameter count, exact quantization precision, training hyperparameters, and safety measures are not documented. Reverse engineering or deep inspection may be necessary.
License & commercial use
MIT license—a permissive OSI-approved license allowing use, modification, and distribution with minimal restrictions (attribution and license copy required).
MIT license permits commercial use. However, the model card includes broad disclaimer language from LM Studio stating it provides no warranty, support, or guarantee of accuracy, security, or fitness for purpose. Commercial deployment requires your own testing, validation, and risk management. No indemnification or SLA is provided. Recommend legal review before production use.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Limited |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No security audit, red-teaming results, or adversarial robustness testing is disclosed. Model can produce offensive or harmful content per disclaimer. No input filtering, output guardrails, or abuse detection are mentioned. For sensitive deployments (e.g., customer-facing, safety-critical), implement external validation, prompt engineering safeguards, and monitoring. Local deployment mitigates some transmission risks but does not address model-level risks.
Alternatives to consider
Qwen2.5-7B / Qwen3-7B (base models)
Same family, unquantized; better documentation and official support. Trade-off: larger disk/memory footprint, no built-in reasoning distillation.
Llama-2-7B-hf or Llama-3-8B (Meta)
Larger community, more examples and serving options. Trade-off: reasoning capability may be weaker; also requires evaluation on your use case.
Mistral-7B or Mistral-Nemo (Mistral AI)
Well-maintained, strong instruction-following, permissive license. Trade-off: no explicit reasoning distillation; smaller context (32k for 7B variant).
Ship DeepSeek-R1-0528-Qwen3-8B-GGUF with senior software developers
This model is ideal for organizations seeking private, reasoning-capable LLM inference on-device. Start with LM Studio for rapid prototyping, then integrate via llama.cpp or custom serving. However, conduct thorough security and accuracy testing before production. Consult the Devco team to design a deployment strategy aligned with your data governance and performance requirements.
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DeepSeek-R1-0528-Qwen3-8B-GGUF FAQ
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Work with a software development agency
Need help beyond evaluating DeepSeek-R1-0528-Qwen3-8B-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Evaluate DeepSeek-R1-0528-Qwen3-8B for Your AI Pipeline
This model is ideal for organizations seeking private, reasoning-capable LLM inference on-device. Start with LM Studio for rapid prototyping, then integrate via llama.cpp or custom serving. However, conduct thorough security and accuracy testing before production. Consult the Devco team to design a deployment strategy aligned with your data governance and performance requirements.