Qwen2.5-Coder-7B-Instruct-bnb-4bit
Qwen2.5-Coder-7B-Instruct-bnb-4bit is a 7.8B parameter code-focused language model quantized to 4-bit precision by Unsloth. It supports 131K token context, excels at code generation and reasoning, and is optimized for efficient fine-tuning and inference on consumer hardware. Licensed under Apache 2.0 (permissive OSI), it is freely available without gating.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | 7.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 97.2k |
| Likes | 12 |
| Last updated | 2024-11-12 |
| Source | unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit |
What Qwen2.5-Coder-7B-Instruct-bnb-4bit is
A transformer-based causal LM with 28 layers, GQA (4 KV heads, 28 Q heads), RoPE positional encoding, and YaRN length extrapolation for up to 131K context. The base model (Qwen/Qwen2.5-Coder-7B-Instruct) is quantized to 4-bit via bitsandbytes by Unsloth for memory efficiency. Trained on 5.5T tokens including code, text-code grounding, and synthetic data. Last modified 2024-11-12.
Run Qwen2.5-Coder-7B-Instruct-bnb-4bit locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit")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
**Inference (4-bit):** ~4–6 GB VRAM (T4, RTX 3060, RTX 4060, M-series Mac). **Fine-tuning (LoRA/QLoRA on single GPU):** ~6–12 GB VRAM (T4, A10, RTX 4090). **No-quantization inference (fp16):** 16–20 GB VRAM (A100 40GB, RTX 6000). Estimates assume batch size 1 and standard transformers + bitsandbytes stack; verify with `nvidia-smi` and model profilers.
Unsloth-packaged model is optimized for LoRA/QLoRA fine-tuning. Model card highlights 2–3.9× speedup and 50–74% memory reduction on Colab. Free Qwen2.5 Colab notebooks provided for supervised fine-tuning and conversational templates (ShareGPT, Vicuna). QLoRA feasible on single T4; LoRA on A10 or higher. Export to GGUF, vLLM, or HuggingFace after fine-tuning.
When to avoid it — and what to weigh
- Real-time, sub-100ms latency requirements — 7B 4-bit quantization on consumer GPUs typically achieves 10–50 tokens/sec. For sub-second response times, consider smaller models (1.5B) or specialized inference engines with aggressive batching.
- Specialized domains requiring retraining from scratch — While fine-tuning is efficient, if your domain fundamentally diverges from code/math/general text, a custom-trained smaller model may be more cost-effective long-term.
- Deployment without container/orchestration infrastructure — Requires transformers ≥4.37.0, bitsandbytes, and careful dependency management. Uncontainerized deployment on shared systems risks version conflicts.
- Edge devices with <8GB VRAM — 4-bit quantization still requires ~4–6GB VRAM for inference. Smaller models (1.5B Qwen2.5-Coder or llama2.c) are needed for tighter constraints.
License & commercial use
Apache License 2.0 (OSI-approved, permissive). Allows commercial use, modification, and distribution with minimal restrictions (retain license and copyright notice).
Commercial use is permitted under Apache 2.0. No gating or special agreements required. Verify compliance with your legal team if embedding in proprietary applications. Base model is Alibaba's Qwen2.5-Coder; Unsloth provides quantized redistribution. No additional commercial licensing barriers identified in provided data.
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 risks: model outputs may contain code injection patterns, hallucinated APIs, or unsafe practices if prompted adversarially. 4-bit quantization does not introduce new vulnerabilities but reduces interpretability for auditing. No security audits or red-teaming results provided. Deploy with input/output validation and sandboxing for untrusted user prompts. Bitsandbytes quantization is widely adopted; no known CVEs flagged.
Alternatives to consider
Qwen2.5-Coder-1.5B-Instruct
Same code capabilities, 78% fewer parameters. Better for resource-constrained inference; fine-tuning on CPU feasible. Trade-off: lower reasoning depth on complex problems.
DeepSeek-Coder-7B-Instruct
Similar 7B size, code-focused, permissive license. Community-driven. Compare on code benchmarks; varies by task domain.
CodeLlama-7B-Instruct
Meta-backed, mature ecosystem. Llama 2 license (non-OSI commercial clause). Larger community, but more restrictive licensing than Apache 2.0.
Ship Qwen2.5-Coder-7B-Instruct-bnb-4bit with senior software developers
Start with our free Colab notebooks for fine-tuning, or contact us to integrate this model into your private LLM infrastructure. Apache 2.0 licensing ensures full commercial flexibility.
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-7B-Instruct-bnb-4bit FAQ
Can I use this model commercially in a closed-source product?
What GPU do I need to run this model?
Does this support long documents (>32K tokens)?
How much faster is fine-tuning with Unsloth?
Custom software development services
Need help beyond evaluating Qwen2.5-Coder-7B-Instruct-bnb-4bit? 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.
Ready to Deploy Qwen2.5-Coder for Code Generation?
Start with our free Colab notebooks for fine-tuning, or contact us to integrate this model into your private LLM infrastructure. Apache 2.0 licensing ensures full commercial flexibility.