Qwen2.5-Coder-1.5B
Qwen2.5-Coder-1.5B is a 1.5 billion parameter code-focused language model from Alibaba's Qwen team. It is designed for code generation, reasoning, and fixing tasks, built on the Qwen2.5 base model with 32K token context. The model is open-source under Apache 2.0, ungated, and intended for development, research, and deployment in resource-constrained environments. It requires transformers ≥4.37.0 and is not recommended for conversational use in its base form without fine-tuning.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 1.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 245.8k |
| Likes | 94 |
| Last updated | 2024-11-18 |
| Source | Qwen/Qwen2.5-Coder-1.5B |
What Qwen2.5-Coder-1.5B is
Architecture: Transformer with RoPE, SwiGLU, RMSNorm, attention QKV bias, tied embeddings. Parameters: 1.54B total (1.31B non-embedding). Layers: 28. Attention heads: 12 (Q) / 2 (KV) GQA. Context: 32,768 tokens. Training: Pretraining on 5.5T tokens including source code, text-code grounding, and synthetic data. Format: Hugging Face Transformers, safetensors. Last updated: 18 Nov 2024. Download count: 245K+.
Run Qwen2.5-Coder-1.5B 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")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 (requires verification against your workload): ~6–8 GB GPU VRAM (float16/bfloat16) for inference; ~12–16 GB for fine-tuning (LoRA/QLoRA reduces to ~4–6 GB). CPU inference feasible for non-real-time use. See Qwen documentation for detailed throughput benchmarks per GPU type.
Model is in pretraining stage and not instruction-tuned. Suitable for SFT (supervised fine-tuning) to add instruction-following or domain-specific behavior. QLoRA feasible on consumer GPUs (≥6GB VRAM). No explicit LoRA adapter release mentioned in card; standard Hugging Face or Axolotl pipelines expected to work with transformers ≥4.37.0. Continued pretraining on domain code also viable.
When to avoid it — and what to weigh
- Production conversation/chat use without post-training — Model card explicitly states: 'We do not recommend using base language models for conversations.' Requires SFT/RLHF or instruction-tuning before deployment as a chatbot.
- Extreme latency constraints on CPU-only systems — While compact, inference on CPU will be slow for real-time use cases. GPU or specialized hardware (e.g., TPU) recommended for production throughput.
- Proprietary, closed-ecosystem deployments without license review — Although Apache 2.0 is permissive, internal legal/compliance review required before embedding in closed products. See commercial use section.
- Use cases requiring very long context in single pass — 32K token context is adequate for most code tasks but insufficient for analyzing entire large codebases or very long reasoning chains without chunking.
License & commercial use
Apache License 2.0. Permissive open-source license allowing use, modification, and distribution with attribution and liability disclaimer. No gating. Model weights and code available without registration.
Apache 2.0 is an OSI-approved permissive license that permits commercial use, including closed-source derivatives and SaaS offerings, provided you include the license and original attribution. No explicit prohibitions on commercial deployment found in model card. However, best practice: (1) Confirm any derivative/service terms with your legal team; (2) Retain Apache 2.0 headers if you modify weights/code; (3) Check for any Qwen organization service-level agreements if using official endpoints. No proprietary license restrictions noted.
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 | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM considerations: (1) Model is a pretrained base model; no explicit safety fine-tuning or guardrails documented—implement content filtering for production use. (2) No mention of red-teaming or adversarial robustness evaluation in card. (3) Code generation models may reproduce training data or generate insecure code patterns; validate outputs in safety-critical contexts. (4) No known supply-chain or model-injection vulnerabilities disclosed. (5) Recommend running in isolated environments with input/output validation.
Alternatives to consider
Mistral 7B (Mistral AI)
Larger (7B), general-purpose, Apache 2.0. Better for conversational tasks and longer-context reasoning; requires more compute. Good alternative if you need conversation capability or larger model.
CodeLlama-7B (Meta)
Code-specialized, 7B parameters, permissive license (Llama 2 community). Larger and mature, but Qwen2.5-Coder is reported as more recent and competitive in benchmarks. Use if you prefer Meta ecosystem.
OpenELM-1B (Apple / OLMo foundation)
Comparable size (~1B), open training methodology, efficient. General-purpose rather than code-optimized. Consider if you prioritize interpretability and want to avoid Alibaba dependency.
Ship Qwen2.5-Coder-1.5B with senior software developers
Qwen2.5-Coder-1.5B is ideal for on-device code completion, private deployments, and fine-tuned code agents. Start with our hardware calculator, review the Apache 2.0 license terms with your team, and prototype fine-tuning using our recommended quantization guides. Contact our team for deployment architecture consulting.
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Qwen2.5-Coder-1.5B FAQ
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Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like Qwen2.5-Coder-1.5B. 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 a Lightweight Code LLM?
Qwen2.5-Coder-1.5B is ideal for on-device code completion, private deployments, and fine-tuned code agents. Start with our hardware calculator, review the Apache 2.0 license terms with your team, and prototype fine-tuning using our recommended quantization guides. Contact our team for deployment architecture consulting.