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 local inference. It is maintained by community contributor bartowski and offers 24 quantization options (ranging from 4.7GB to 29.55GB) to balance quality and resource constraints. The model is designed for code generation, understanding, and instruction-following tasks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | bartowski |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 35.8k |
| Likes | 52 |
| Last updated | 2024-11-09 |
| Source | bartowski/Qwen2.5-Coder-14B-Instruct-GGUF |
What Qwen2.5-Coder-14B-Instruct-GGUF is
This is a GGUF-quantized derivative of Qwen/Qwen2.5-Coder-14B-Instruct, quantized using llama.cpp (release b4014) with imatrix calibration. The model supports the Qwen chat prompt format (<|im_start|>/<|im_end|> tokens). Multiple quantization levels are provided (Q2_K through F16), enabling deployment on hardware from ~5GB RAM (IQ2_XS) to systems with 30GB+ VRAM. No explicit context length or parameter count stated in the card; defaults should be verified against the base model documentation. Quantization was performed without official Alibaba involvement—this is a community repackaging.
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="bartowski/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 (verify against your quantization choice): Q4_K_M (~9GB) requires ~10–12GB VRAM for inference with reasonable context window (typically 8–16k tokens). Q5_K_M (~10.5GB) needs ~12–14GB. IQ2_XS (~4.7GB) fits ~6–8GB systems but quality degrades significantly. F16 full precision (29.55GB) requires 32GB+ VRAM or aggressive batching/offloading. CPU inference possible via llama.cpp with reduced speed; offloading to consumer GPU (RTX 3060, A6000) feasible with most quantization levels.
Unknown. The card does not discuss fine-tuning, LoRA, or QLoRA compatibility. The base model (Qwen/Qwen2.5-Coder-14B-Instruct) may support these, but applying LoRA on top of GGUF quantized weights is non-standard and requires custom tooling. Recommend re-quantizing after fine-tuning on the original full-precision checkpoint if adaptation is required.
When to avoid it — and what to weigh
- Need for production-grade SLA/support — This is a community quantization with no official support channel, update cadence guarantee, or vendor SLA. Use only where internal ops can manage updates and debugging.
- Requiring state-of-the-art code performance — Quantization introduces quality loss. For mission-critical code generation (security review, safety-critical systems), benchmark against unquantized Qwen2.5-Coder or larger models (e.g., GPT-4, Claude 3.5).
- Heavy concurrent API load (high throughput) — GGUF + llama.cpp are optimized for single-user or small-batch inference. For multi-tenant or high-RPS scenarios, vLLM or TGI backends with full-precision or minimal quantization are more suitable.
- Strict IP/license governance — Apache-2.0 permissive, but the original Qwen2.5-Coder model is proprietary to Alibaba. Review Alibaba's usage terms for your use case (model weights, not code).
License & commercial use
Apache-2.0 (specified in model metadata). This is a permissive, OSI-approved license allowing commercial use, modification, and distribution with attribution and no warranty. The quantization process and GGUF packaging do not alter the underlying license.
Apache-2.0 permits commercial deployment without explicit permission or royalty. However: (1) The base model (Qwen2.5-Coder-14B-Instruct) is proprietary to Alibaba; review Alibaba's model card and EULA for any restrictions on commercial training data use or output re-use. (2) Quantization and packaging by bartowski (community contributor) does not grant additional rights. (3) No indemnification or SLA from bartowski. Recommend legal review of Alibaba's terms and your specific use case (e.g., if you embed outputs in commercial products).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM risks apply: model outputs not guaranteed safe; potential for prompt injection, hallucination, or code generation vulnerabilities. Quantization may amplify artifacts. No security audit of base model or quantization process documented. Local inference reduces data exfiltration risk vs. cloud APIs. Recommend: sandboxing code generation, input validation, output review, and testing for sensitive use cases (e.g., security review tools).
Alternatives to consider
Qwen/Qwen2.5-Coder-14B-Instruct (original, full-precision)
Higher quality; no quantization artifacts. Requires more VRAM (~30GB F16) and inference cost. Better for offline batch processing or well-resourced systems.
deepseek-coder-6.7b or deepseek-coder-33b (quantized variants)
DeepSeek alternative with similar code focus. Various quantizations available. Compare performance and license (DeepSeek license check required) for your domain.
Llama-2-7b-code or similar smaller open-source models
Lighter weight (~3–7GB quantized). Lower quality than Qwen 14B but faster and more resource-efficient for edge/embedded scenarios.
Ship Qwen2.5-Coder-14B-Instruct-GGUF with senior software developers
Download Qwen2.5-Coder-14B-Instruct GGUF and run code generation on your infrastructure. Choose your quantization level, integrate with llama.cpp or LM Studio, and start analyzing or generating code privately. Check the full model card for download and prompt format details.
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Qwen2.5-Coder-14B-Instruct-GGUF FAQ
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Custom software development services
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Ready to deploy local code intelligence?
Download Qwen2.5-Coder-14B-Instruct GGUF and run code generation on your infrastructure. Choose your quantization level, integrate with llama.cpp or LM Studio, and start analyzing or generating code privately. Check the full model card for download and prompt format details.