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 format for local/self-hosted deployment. It supports up to 32,768 token context (131K with vLLM YARN), trained on 5.5 trillion tokens including source code and synthetic data. Designed for code generation, reasoning, and fixing with instruction-tuning for chat-like interaction. Apache 2.0 licensed, ungated, and optimized for inference via llama.cpp.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 64.9k |
| Likes | 162 |
| Last updated | 2024-11-12 |
| Source | Qwen/Qwen2.5-Coder-14B-Instruct-GGUF |
What Qwen2.5-Coder-14B-Instruct-GGUF is
Causal transformer LM with 48 layers, 40 query heads and 8 KV heads (GQA), RoPE positional encoding, SwiGLU activation, RMSNorm. 13.1B non-embedding parameters. Offers 8 quantization variants (q2_K through q8_0) to trade speed/quality for memory footprint. GGUF format is CPU/GPU-agnostic; optimized inference in llama.cpp. Context length 32K natively; 128K supported only via vLLM with YARN extrapolation.
Run Qwen2.5-Coder-14B-Instruct-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen2.5-Coder-14B-Instruct-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): Q5_K_M (recommended quantization) ~14–18 GB VRAM for GPU, or ~20–25 GB system RAM for CPU inference. Lower quantizations (q2_K, q3_K_M) ~7–12 GB; higher (q8_0) ~25–30 GB. Throughput details available in Qwen docs but exact specs unknown. Verify benchmarks at https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html before deployment.
Unknown. Model card does not document LoRA/QLoRA fine-tuning feasibility. GGUF format is primarily inference-optimized; standard fine-tuning likely requires non-quantized base model (Qwen/Qwen2.5-Coder-14B-Instruct). Recommend checking Qwen GitHub / docs for training recipes and PEFT compatibility.
When to avoid it — and what to weigh
- Need 128K+ Context in Production — GGUF variant is limited to 32K context window. Full 128K support requires non-GGUF models with vLLM. GGUF + llama.cpp does not yet expose YARN extrapolation in stable releases.
- Require Sub-100ms Inference SLA — 14B model with lower quantizations (q2_K, q3_K_M) may struggle to meet stringent latency targets without GPU acceleration. Throughput benchmarks referenced in docs but specific numbers unknown.
- Limited to Pre-built LLM APIs or SaaS — This model requires self-hosting, GGUF compilation, and inference toolchain (llama.cpp, vLLM). No managed endpoint provided by Qwen; requires engineering effort.
- Enterprise Indemnification / SLA Guarantees — Open-source model with no commercial support, SLA, or liability indemnification from Alibaba. Use cases requiring vendor-backed guarantees should seek commercial alternatives.
License & commercial use
Apache License 2.0 (SPDX: apache-2.0). Permissive OSI-approved open-source license permitting use, modification, and distribution with minimal restrictions.
Apache 2.0 is a permissive license that explicitly allows commercial use, provided LICENSE and NOTICE files are included and modifications are documented. No restriction to non-commercial use. However, no warranty or liability protection is provided; commercial users assume all risk. Verify terms with Alibaba Cloud legal if entering a regulated industry (healthcare, finance, etc.) or deploying at scale.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit or threat model stated in card. GGUF is a binary format; verify provenance and integrity via published checksums. Model weights from Hugging Face; use official download links. Consider input validation and output monitoring in production (code generation may produce harmful or insecure code). No built-in content filtering documented; implement guardrails at application layer if needed.
Alternatives to consider
Meta Llama 3.1 70B / Code Llama 70B
Larger, broader code/general capability; multi-language support. Llama 2 Community License is non-commercial restricted; Llama 3.1 license permits commercial use. Requires more VRAM; less nimble for local deployment.
DeepSeek-Coder-33B-Instruct
Larger 33B model, strong code reasoning. MIT/Apache 2.0 licensed. Similar self-hosting footprint; stronger on math/reasoning but less widely integrated with inference tooling.
OpenAI GPT-4 / GitHub Copilot
Proprietary SaaS, highest capability, managed uptime/compliance. No local hosting; data leaves premise. Suitable if commercial support and regulatory compliance matter more than cost/control.
Ship Qwen2.5-Coder-14B-Instruct-GGUF with senior software developers
Download the GGUF model, install llama.cpp, and start running code generation on your hardware. Full documentation and quantization options available.
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Qwen2.5-Coder-14B-Instruct-GGUF FAQ
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What is the difference between this GGUF variant and the base model?
Work with a software development agency
Need help beyond evaluating Qwen2.5-Coder-14B-Instruct-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.
Deploy Qwen2.5-Coder-14B locally—no cloud dependency.
Download the GGUF model, install llama.cpp, and start running code generation on your hardware. Full documentation and quantization options available.