Qwen2.5-Coder-1.5B-Instruct-GGUF
Qwen2.5-Coder-1.5B-Instruct-GGUF is a 1.5 billion parameter open-source code-generation model from Alibaba's Qwen team, pre-quantized in GGUF format for efficient local deployment. It supports up to 32,768 token context and is designed for code generation, reasoning, and fixing tasks. The model is permissively licensed under Apache 2.0 and requires modest hardware (suitable for CPU or entry-level GPU inference).
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 | 54.1k |
| Likes | 70 |
| Last updated | 2024-11-12 |
| Source | Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF |
What Qwen2.5-Coder-1.5B-Instruct-GGUF is
Causal language model with 1.54B parameters (1.31B non-embedding), 28 transformer layers, grouped query attention (12 Q heads, 2 KV heads), RoPE positional encoding, SwiGLU activation, and RMSNorm. Trained on 5.5T tokens including source code, text-code grounding, and synthetic data. GGUF quantizations available: q2_K through q8_0. Context window 32,768 tokens (full YARN extrapolation to 131,072 supported only in non-GGUF vLLM). Instruction-tuned post-training for chat/agent tasks.
Run Qwen2.5-Coder-1.5B-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-1.5B-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: q4_K_M quantization ~3–4 GB VRAM (GPU) or ~6–8 GB system RAM (CPU). q2_K variant reduces to ~1.5–2 GB; q8_0 increases to ~5–6 GB. Inference throughput not stated; refer to official benchmark link. For multi-user serving, plan ~2–4 GB overhead per concurrent session.
Not explicitly addressed in card. Standard practice: LoRA/QLoRA feasible on GGUF-quantized models via quantization-aware fine-tuning frameworks (e.g., bitsandbytes + transformers), but post-training requires conversion back to non-quantized checkpoint and re-quantization. Consult Qwen documentation for instruction tuning recipes.
When to avoid it — and what to weigh
- Production High-Volume Inference Requiring Maximum Quality — 1.5B parameter models trade depth for speed. For mission-critical code generation, larger variants (7B, 14B, 32B) or commercial models (GPT-4, Claude) provide higher accuracy at cost of inference latency/resources.
- Context-Heavy Code Understanding Tasks — 32K token window is modest for repository-scale understanding. YARN extrapolation to 131K is unavailable in GGUF (only via non-quantized vLLM), limiting multi-file analysis without windowing strategies.
- Inference at Scale Without Infrastructure — GGUF optimization targets local/on-prem deployment. Cloud-native batch inference at scale requires TGI, vLLM, or similar framework deployments; GGUF ecosystem is narrower (primarily llama.cpp).
- Security-Critical Code in Regulated Industries — No formal security audit or compliance certification stated. Use in HIPAA/PCI/SOC2-bound environments requires independent assessment and vendor support, which is not addressed in the model card.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, and redistribution with attribution and liability/warranty disclaimers.
Apache 2.0 is a permissive OSI-approved license that explicitly allows commercial use without royalties. No runtime fees or usage restrictions stated. However, ensure compliance with any underlying training data licenses (Qwen card does not enumerate data sources). Consult legal if model outputs are used in regulated industries (e.g., medical code generation, financial systems) or high-stakes contexts without independent security validation.
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 | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No formal security audit or threat model disclosed. Consider: (1) GGUF quantization may obscure model weights but does not provide formal integrity verification; (2) llama.cpp and quantization libraries are open-source and community-reviewed, not formally certified; (3) deployment on untrusted endpoints risks prompt injection, data exfiltration, or model theft; (4) no stated sanitization of code generation outputs—model may reproduce training data or generate insecure code patterns. Independent security review recommended before production use in sensitive domains.
Alternatives to consider
DeepSeek-Coder-1.3B
Similar size, code-focused. Compare performance on your benchmark tasks and ecosystem support (vLLM vs. llama.cpp).
Qwen2.5-Coder-7B or 14B
If model accuracy is insufficient, larger variants from same family trade inference speed for higher quality. 14B rivals GPT-3.5 in some benchmarks (per model card claims).
Mistral 7B Instruct (generic) or CodeLlama-7B
Broader community, more tutorials/integrations. Trade code specialization for generalist capability or use CodeLlama for code-only focus.
Ship Qwen2.5-Coder-1.5B-Instruct-GGUF with senior software developers
Qwen2.5-Coder-1.5B-Instruct-GGUF enables on-device code generation without external APIs or compliance risk. Start with llama.cpp, evaluate on your codebase, and integrate into your development workflows. Check our guides for self-hosted LLM deployment and custom AI app architecture.
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Qwen2.5-Coder-1.5B-Instruct-GGUF FAQ
Can I use this commercially without paying royalties or requesting permission?
What GPU/hardware do I need?
Can I extend the context beyond 32K tokens?
Is the model trained on copyrighted code, and can I be sued for using its outputs?
Work with a software development agency
From first prototype to production, DEV.co delivers software development services around tools like Qwen2.5-Coder-1.5B-Instruct-GGUF. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to Deploy Code Intelligence Locally?
Qwen2.5-Coder-1.5B-Instruct-GGUF enables on-device code generation without external APIs or compliance risk. Start with llama.cpp, evaluate on your codebase, and integrate into your development workflows. Check our guides for self-hosted LLM deployment and custom AI app architecture.