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 footprint. It is instruction-tuned, supports up to 131K token context (with YaRN extrapolation), and is designed for code generation, reasoning, and fixing tasks. The model is open-source under Apache 2.0, ungated, and can be deployed on consumer/mid-range GPUs.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 14.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 1M |
| Likes | 21 |
| Last updated | 2025-01-12 |
| Source | Qwen/Qwen2.5-Coder-14B-Instruct-AWQ |
What Qwen2.5-Coder-14B-Instruct-AWQ is
A transformer-based causal language model with 48 layers, grouped query attention (40 Q-heads, 8 KV-heads), RoPE positional embeddings, SwiGLU activation, and RMSNorm. AWQ 4-bit quantization reduces parameters from 14.7B to approximately 3.7GB in-memory footprint. Trained on 5.5T tokens including code, text-code pairs, and synthetic data. Supports context extension via YaRN. Compatible with HuggingFace transformers (>=4.37.0), vLLM, and standard inference frameworks. Last updated January 2025.
Run Qwen2.5-Coder-14B-Instruct-AWQ 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-AWQ")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: AWQ 4-bit quantization reduces the model to ~3.7–4.5 GB. Single-GPU inference requires approximately 6–10 GB VRAM (A10, RTX 3080, or equivalent) for batch size 1–4. Multi-GPU or distributed serving scales linearly. CPU inference is not practical. vLLM is recommended for optimal throughput and memory efficiency.
LoRA/QLoRA fine-tuning is feasible on the quantized model for task-specific instruction tuning (e.g., internal coding standards, domain-specific languages). QLoRA (quantized LoRA) reduces additional memory overhead to <2GB on 24GB GPUs. Full fine-tuning would require de-quantization and is not recommended for production. Adaptation time: 2–8 hours on a single high-end GPU for 10K instruction pairs.
When to avoid it — and what to weigh
- Bleeding-edge performance on very recent programming languages — Training data cutoff is not explicitly stated in the card. Verify against your target language versions and framework updates to avoid outdated code patterns or API recommendations.
- Latency-critical real-time systems with strict SLA (<50ms response time) — Even quantized 14B models require batching or careful optimization to meet ultra-low latency. For single-token production serving at <50ms, consider smaller 3B or 7B alternatives, or GPU-intensive larger models with specialized inference.
- Non-English code or multilingual programming documentation — Tags indicate English ('en') focus. If you require strong multilingual or non-ASCII code support (e.g., CJK variable names, non-Latin scripts), test comprehensively or consider multilingual models.
- Hardware-constrained environments (e.g., embedded, CPU-only inference) — AWQ quantization targets GPU inference. CPU inference on 14B is impractical without further optimization (e.g., llama.cpp). For embedded use, evaluate 1.5B or 3B variants instead.
License & commercial use
Apache 2.0 license. This is a permissive, OSI-approved open-source license that permits commercial use, modification, and redistribution under the terms stated in the license. The model is ungated and freely downloadable.
Apache 2.0 explicitly permits commercial use, including proprietary products and services, provided you comply with attribution and license notice requirements. No additional commercial licensing, usage fees, or Alibaba approval is required. However, verify that any hosting platform (e.g., Azure, cloud providers) does not impose additional terms. Recommend consulting legal review if deploying at scale in enterprise settings to ensure compliance with attribution and any downstream liability clauses.
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 |
Standard LLM security considerations: (1) Model runs inference locally, reducing data exposure if self-hosted, but verify host security. (2) No known hardened mitigations against adversarial prompts; treat as any untrusted neural network inference—validate outputs in critical paths. (3) Quantization artifacts are not documented; no formal adversarial robustness claims. (4) Ensure dependency security for transformers, torch, and vLLM. (5) If deployed via API, implement rate limiting and input validation. (6) Code generation output must be reviewed; the model can produce non-functional or insecure code patterns. No formal security audit mentioned in the card.
Alternatives to consider
DeepSeek-Coder-33B-Instruct
Open-source code-specialized model; 33B parameter size offers stronger reasoning but requires more VRAM (~40GB unquantized). Better for offline deployment with ample resources; trade-off is reduced efficiency vs. Qwen2.5-Coder-14B.
Mistral-7B-Instruct
Smaller, more efficient general-purpose instruction model; easier to deploy on CPU/edge but weaker on code-specific tasks. Suitable if you prioritize low latency and light resource use over specialized coding capability.
Gpt-4o (OpenAI API)
Proprietary, closed-source. Strongest code capability and fastest inference via managed API. Requires external API calls, higher cost, and data leaves your infrastructure. Choose if model capability and ease of integration outweigh privacy/cost concerns.
Ship Qwen2.5-Coder-14B-Instruct-AWQ with senior software developers
Qwen2.5-Coder-14B-Instruct-AWQ offers strong code capabilities, low latency, and full control over your data. Use our private LLM service to deploy in minutes, or contact our team to integrate it into your CI/CD or IDE.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
Qwen2.5-Coder-14B-Instruct-AWQ FAQ
Can I use this model in a commercial product without paying Alibaba or obtaining special permission?
What GPU do I need to run this model?
Can I fine-tune this model on my own code?
Does this model support 131K token context out of the box?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Qwen2.5-Coder-14B-Instruct-AWQ is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Self-Hosted Code LLM?
Qwen2.5-Coder-14B-Instruct-AWQ offers strong code capabilities, low latency, and full control over your data. Use our private LLM service to deploy in minutes, or contact our team to integrate it into your CI/CD or IDE.