Qwen

When to Use

User needs Qwen to work reliably for chat, coding, reasoning, structured outputs, or vision. Agent handles surface selection, live model verification, hosted-versus-local tradeoffs, and failure recovery before the workflow reaches production.

Architecture

Memory lives in ~/qwen/. If ~/qwen/ does not exist, run setup.md. See memory-template.md for structure.

~/qwen/
├── memory.md         # Status, activation rules, and deployment defaults
├── routes.md         # Preferred route per workload
├── servers.md        # Known local or hosted endpoints
├── experiments.md    # Prompt, parser, and latency notes
└── logs/             # Optional sanitized repro payloads

Quick Reference

Use the smallest file that resolves the blocker.

Requirements

• curl and jq for minimal endpoint checks
• Hosted Qwen usually needs a DASHSCOPE_API_KEY
• Self-hosted Qwen may use Ollama, vLLM, SGLang, or another OpenAI-compatible server
• Keep secrets in environment variables only

Core Rules

1. Lock the Surface Before Tuning the Model

• Identify the real execution surface first: Alibaba Model Studio hosted API, another OpenAI-compatible provider, or a self-hosted server.
• Most "Qwen issues" are actually endpoint, region, server, or chat-template issues rather than model quality issues.

2. Verify Live Availability Before Naming Any Model

• Start with a /models or equivalent health check and copy the live model ID from the response.
• Never trust stale screenshots, old blog posts, or remembered IDs for production routing.

3. Route by Workload, Not by Brand Loyalty

• Split the request into one of these paths: fast chat, deep reasoning, coding agent, deterministic JSON, or vision.
• Pick the smallest Qwen family and server path that can reliably do that job.

4. Treat Structured Output as a Separate Reliability Problem

• If Qwen is feeding tools, JSON, or downstream writes, use strict schemas, low temperature, and parser validation before acting.
• If the first pass is creative or reasoning-heavy, add a second deterministic normalization pass instead of forcing one prompt to do both.

5. Separate Model Problems From Server Problems

• When behavior changes after migration, isolate the variable: model family, quantization, chat template, reasoning mode, parser, or backend.
• Reproduce with one minimal payload before changing prompts, infrastructure, and business logic at the same time.

6. Compare Hosted and Self-Hosted Explicitly

• Hosted Qwen usually wins on speed to first success and managed multimodal access.
• Self-hosted Qwen only wins when privacy, local cost control, or offline use clearly outweigh operational overhead.

7. Ask Before Creating Persistent State

• Work statelessly by default.
• Only create ~/qwen/ notes, saved routes, or repro logs after the user wants continuity across Qwen tasks.

Common Traps

• Treating "Qwen" as one interchangeable thing -> hosted APIs, Ollama, vLLM, and agent frameworks behave differently.
• Hardcoding dated model IDs -> region and release cadence make old IDs fail fast.
• Mixing free-form reasoning with strict JSON output -> parsing breaks when one prompt is asked to do both.
• Blaming the model for local slowness -> Apple Silicon and Ollama often fail because of model size, quantization, or oversized context.
• Migrating from another OpenAI-compatible backend without rechecking tool-calling -> parser and chat-template differences can break automation.

External Endpoints

Use only the smallest hosted endpoint that answers the current question.

No other data is sent externally.

Security & Privacy

Data that leaves your machine:

• Prompt content sent to Alibaba Cloud Model Studio when using hosted Qwen
• Optional images or multimodal payloads sent to hosted Qwen vision endpoints when requested

Data that stays local:

• Deployment preferences and routing notes in ~/qwen/ after user approval
• Local server URLs, workload notes, and sanitized repro payloads kept for debugging

This skill does NOT:

• Store API keys in markdown files
• Send data to undeclared third-party endpoints
• Assume local servers are safe to expose publicly
• Modify its own skill files

Scope

This skill ONLY:

• routes Qwen work across hosted and self-hosted execution surfaces
• chooses model families for chat, coding, reasoning, vision, and automation
• debugs migration, parser, latency, and endpoint problems
• stores lightweight local notes only after user approval

This skill NEVER:

• invent live model availability without checking
• persist secrets in ~/qwen/
• execute destructive downstream automation without validated output
• pretend one backend's tool-calling behavior applies everywhere

Trust

Using hosted Qwen sends prompt data to Alibaba Cloud Model Studio.

Only install if you trust that service with your data, or keep Qwen fully self-hosted.

Related Skills

Install with clawhub install if user confirms:

• models — choose model families and cost tiers before locking Qwen into production
• api — debug auth, payloads, retries, and OpenAI-compatible request shapes
• coding — tighten agent coding workflows after the Qwen route itself is stable
• chat — improve conversation shaping once the Qwen route itself is stable
• memory — store durable routing choices and repeated migration lessons

Feedback

• If useful: clawhub star qwen
• Stay updated: clawhub sync