ChatGLM2-6B

Use this repo-specific operating skill when a task names ChatGLM2-6B, the THUDM/chatglm2-6b model, its legacy CLI/Gradio/Streamlit demos, its FastAPI or OpenAI-compatible API samples, C-Eval evaluation, or the ptuning/ training/checkpoint workflow. This skill distills the public repository into self-contained routes; model weights and benchmark datasets are external and are never bundled.

Start here

1. Identify the model source: Hub id, pinned revision, or a complete local model directory. Keep tokenizer and model revisions aligned.
2. Choose the route below and read its linked references before writing a command. Most official scripts assume CUDA and load several-billion- parameter weights; do not use them as cheap import checks.
3. Install the repo's runtime dependencies with the documented requirements, plus service or fine-tuning extras only for the selected workflow. Pin legacy Gradio/Streamlit versions when using the source demos; see references/model-and-environment.md.
4. Run the safe root preflight: python scripts/check_installation.py --backend auto.
5. Read references/troubleshooting.md for cross-cutting model-cache, backend, version, license, and safety issues.

Route by task

• Local chat, streaming, CLI, Gradio, Streamlit, quantization, CPU/MPS, or multi-GPU placement: read sub-skills/chat-and-demos/SKILL.md.
• FastAPI / or OpenAI-compatible /v1/chat/completions, curl clients, SSE, or service deployment: read sub-skills/api-serving/SKILL.md.
• C-Eval JSONL layout, answer extraction, or benchmark scoring: read sub-skills/evaluation/SKILL.md.
• P-Tuning v2, ADGEN/chat data, torchrun, prefix checkpoints, prediction, or optional DeepSpeed: read sub-skills/ptuning/SKILL.md.

When a task spans routes, start with the route that owns the artifact and follow its explicit cross-link. For example, validate chat data in ptuning, then use chat-and-demos to inspect a prefix checkpoint, and finally use api-serving to expose the loaded model.

Installation and verification

This repository is script-oriented rather than a Python distribution; it has no pyproject.toml/setup.py package to install. From a checkout, install the runtime file with a Python version supported by the selected dependency wheels:

python -m pip install -r references/runtime-requirements.txt

The bundled requirements file includes the base runtime plus service, evaluation, and P-Tuning extras used by this skill. If you want a smaller environment, install only the lines required by the selected route. The source demos use legacy Gradio APIs; verify the version before launch and read the environment reference rather than accepting an unbounded latest version. For full-parameter fine-tuning, DeepSpeed is optional and must match the installed PyTorch/CUDA stack.

The bundled scripts/check_installation.py checks dependency metadata, a requested backend, and an optional local model path without downloading or loading weights. It is a preflight, not proof that a multi-billion-parameter model fits. Read references/repo-provenance.md before refreshing the skill against a changed checkout; the provenance file records the source commit, dirty state, model-loading contract, and evidence paths.

Boundaries and safety

• Never claim a successful CPU import proves CUDA, MPS, quantization, or multi-GPU behavior.
• Do not download weights or external datasets from bundled diagnostic scripts. Acquire them through approved sources and validate local paths first.
• Treat the sample services' wildcard CORS and unauthenticated listeners as development defaults. Add authentication, origin controls, rate limits, and timeouts before non-local exposure.
• Respect the model-weight license and the safety restrictions summarized in references/license-and-safety.md.