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name: qwen description: "Use QwenLM/Qwen for Qwen model loading, local generation, serving, fine-tuning, quantization, evaluation, system prompts, tool use, and tokenizer-aware workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Qwen

Use this skill when a task names Qwen, Qwen-7B/Qwen-14B/Qwen-72B, Qwen-Chat, Qwen/Qwen-*, ModelScope Qwen checkpoints, Qwen's Transformers examples, qwenllm/qwen Docker images, or the repository's fine-tuning, evaluation, serving, tokenizer, and tool-use workflows.

This is a repository operating graph, not a general model card. Start by deciding whether the request is about a base model or a chat model, whether the checkpoint is remote or local, and whether the desired action needs CPU, CUDA, Docker, a vendor accelerator, a cloud API, credentials, or a sandbox.

Fast start

• Base install for the documented Transformers workflows: pip install -r requirements.txt.
• Add only the optional dependency family needed by the route: web demo, API server, PEFT/DeepSpeed, AutoGPTQ, vLLM/FastChat, ModelScope, or a vendor runtime.
• Safe environment check: python scripts/check_qwen_environment.py --check-dependencies.
• Minimal public-package check: python -c "import torch, transformers, tiktoken; print(torch.__version__, transformers.__version__)".
• Do not download a checkpoint, start a service, run training, run a benchmark, or use a cloud key merely to prove that the package imports.

Route by task

User request	Read first
Load Qwen with Transformers/ModelScope, chat, batch generation, CPU/multi-GPU, precision, local checkpoints, long context, or quantized inference	sub-skills/inference-model-loading/SKILL.md
Run the CLI/Web UI/OpenAI-compatible API, vLLM/FastChat, Docker, TensorRT, Ascend, or DCU deployment	sub-skills/serving-deployment/SKILL.md
Prepare SFT data, full fine-tuning, LoRA, Q-LoRA, DeepSpeed/FSDP, adapter merge, or GPTQ quantization	sub-skills/finetuning-quantization/SKILL.md
Reproduce C-Eval, MMLU, CMMLU, GSM8K, HumanEval, or plugin/tool-use benchmarks	sub-skills/evaluation-reproduction/SKILL.md
Use system prompts, ReAct, function calling, Hugging Face Agent patterns, ChatML, special tokens, or BPE merge extension	sub-skills/prompting-tool-use-tokenization/SKILL.md

Cross-cutting rules

1. Prefer a local checkpoint path when the user already has one. A model identifier such as Qwen/Qwen-7B-Chat or qwen/Qwen-7B-Chat implies a download and remote-code execution; state that prerequisite explicitly.
2. Keep trust_remote_code=True explicit for these historical Qwen checkpoints. Verify the checkpoint contains its config, tokenizer files, model shards, and any required qwen.tiktoken file before diagnosing Python code.
3. Never treat a CPU import or a tiny CUDA tensor as proof that full model generation, training, vLLM, GPTQ, or benchmark performance works. Report hardware, memory, model size, and optional dependency gates separately.
4. Treat Qwen-* base models and Qwen-*-Chat models differently: base models are for continuation; chat models provide chat/chat_stream and alignment behavior.
5. Use the bundled helpers for dry-run command construction and local validation. They do not load models, download data, launch services, invoke Docker, or execute generated code.

Shared references

• Read references/model-family-overview.md for model names, context, precision, quantization, and backend planning.
• Read references/troubleshooting.md for install/import, checkpoint, tokenizer, optional dependency, hardware, license, and safety failures.
• Read references/repo-provenance.md before deciding whether this graph is stale for a newer checkout.
• Read references/repo-routing-metadata.json only when maintaining managed router placement; it is structured metadata, not a prose route.

Scope and safety

The repository contains historical GPU-heavy and vendor-specific workflows. Keep network, credentials, Docker, public sharing, generated-code execution, model downloads, training, and long-running servers explicit and user-approved. Preserve Qwen's license and safety obligations and perform application-specific red-teaming before deployment.