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Secure AI virtual data roomsQwen2.5-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 fixing with support for up to 128K token context. Licensed under Apache-2.0, it is freely available and designed for developers building code-focused AI applications, agents, or self-hosted solutions.
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 | 4M |
| Likes | 172 |
| Last updated | 2025-01-12 |
| Source | Qwen/Qwen2.5-Coder-14B-Instruct |
What Qwen2.5-Coder-14B-Instruct is
A causal language model with 48 transformer layers, GQA (40 Q-heads, 8 KV-heads), RoPE/SwiGLU/RMSNorm architecture, and 13.1B non-embedding parameters. Supports 131K context via YaRN length extrapolation (static scaling in vLLM). Trained on 5.5T tokens including source code, text-code grounding, and synthetic data. Requires transformers ≥4.37.0 and uses HuggingFace safetensors format.
Run Qwen2.5-Coder-14B-Instruct 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")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: ~28–36 GB VRAM for bfloat16 inference (14.7B × 2 bytes + activations/kv-cache). Full fp32 not practical. Model card references memory/throughput benchmarks in documentation; exact figures not provided here. vLLM deployment recommended for production.
Model is instruction-tuned; further SFT via LoRA/QLoRA is feasible on consumer hardware (~24 GB VRAM). No LoRA/QLoRA artifacts or guidance provided in model card. Base model (Qwen2.5-Coder-14B) is available separately for pretraining or continued training. Standard HuggingFace transformers compatibility.
When to avoid it — and what to weigh
- Gated or commercial redistribution concerns — Not applicable—Apache-2.0 is permissive. However, verify your deployment and terms of service if reselling model access.
- Requirement for sub-second latency at scale without optimization — 14B model requires significant VRAM and batching. Model card recommends vLLM for deployment but does not guarantee latency SLAs. Benchmark documentation referenced but not provided in data.
- Need for non-English code or multilingual reasoning — Model card emphasizes English tags and general training approach; multilingual code-specific performance is unstated. Requires evaluation for non-English codebases.
- Requirement for GPT-4o-level performance at 14B scale — Model card claims 32B variant matches GPT-4o; 14B capabilities relative to larger/closed models are unspecified. Benchmarks referenced in blog but not provided in this data.
License & commercial use
Apache-2.0: permissive open-source license. No copyleft or commercial use restrictions. Redistribution and modifications are allowed with license retention.
Apache-2.0 permits commercial use, modification, and redistribution without royalties or special permissions. No gated access or usage restrictions. However, ensure your deployment, ToS, and data handling comply with applicable regulations. No official commercial support or SLA mentioned in provided data.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, threat model, or vulnerability disclosure data provided. Model is open-source (code inspection possible) and uses standard transformers architecture. Risks: prompt injection, code generation bias toward insecure patterns, resource exhaustion (long-context requests). Deployment should include input validation, output filtering, and resource limits. Self-hosted deployments avoid data exfiltration to external APIs.
Alternatives to consider
Qwen2.5-Coder-32B-Instruct
Same family, ~2× parameters; model card claims GPT-4o-level performance. Requires ~72 GB VRAM; trade-off between accuracy and resource cost.
Qwen2.5-Coder-7B-Instruct
Smaller variant (~14 GB VRAM); faster inference for latency-sensitive applications. Same training approach; reduced model capacity for complex tasks.
CodeLlama-34B / Mistral-7B (via OpenWebUI or similar)
Established alternatives with different architectures/training. CodeLlama optimized for code but less recent; Mistral offers strong general reasoning with code capability.
Ship Qwen2.5-Coder-14B-Instruct with senior software developers
Explore self-hosted code generation with Qwen2.5-Coder-14B. Download from HuggingFace, run on vLLM, and build code agents tailored to your infrastructure. Contact our team to plan GPU allocation and integration.
Talk to DEV.coRelated open-source tools
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Qwen2.5-Coder-14B-Instruct FAQ
Can I use this model commercially without paying for a license?
What GPU hardware do I need to run this model?
Does this model support 128K context out-of-the-box?
How does this compare to GPT-4o for code tasks?
Custom software development, end to end
Evaluating Qwen2.5-Coder-14B-Instruct is the easy part; running it is not. DEV.co offers software development services and AI software development services for open-source llms teams that need a partner past the proof of concept.
Ready to Deploy Code LLMs?
Explore self-hosted code generation with Qwen2.5-Coder-14B. Download from HuggingFace, run on vLLM, and build code agents tailored to your infrastructure. Contact our team to plan GPU allocation and integration.