Baichuan2

Use this repo skill when a task is about Baichuan2 model selection, chat/base inference, local chat demos, OpenAI-compatible serving, quantized deployment, CPU deployment, checkpoint conversion, or supervised fine-tuning.

Baichuan2 is a model-family repository rather than an importable Python package. The operating guidance is therefore organized around public workflows and bundled helpers, not a package module tree.

Start here

1. If the task may depend on repo freshness, read references/repo-provenance.md.
2. If the user has not chosen a checkpoint, read references/model-overview.md.
3. Prepare the smallest environment for the requested workflow using references/installation.md.
4. Run a no-weight dependency check before loading 7B/13B weights:

python scripts/check_baichuan2_env.py --workflow inference
python scripts/check_baichuan2_env.py --workflow all --require-cuda

Use --workflow inference, deployment, fine-tuning, or all to match the route. These checks do not download model weights.

Route by user request

User request signals	Read	Why
model.chat, Python inference, Base-model generation, terminal demo, Streamlit web UI, OpenAI-compatible /v1/chat/completions	sub-skills/inference/SKILL.md	Owns Baichuan2 Chat/Base inference, CLI/web demos, and the Flask chat-completions helper.
4-bit/8-bit quantization, BitsAndBytes, CPU-only loading, load_in_8bit, quantize(4), lm_head.weight, Baichuan1 optimization migration	sub-skills/deployment/SKILL.md	Owns memory-reduction, CPU deployment, and checkpoint-conversion workflows.
supervised fine-tuning, DeepSpeed, LoRA, fine-tune.py, hostfile, W_pack, ds_config, training data schema	sub-skills/fine-tuning/SKILL.md	Owns SFT data validation, ZeRO-3 launch planning, LoRA, and post-training loading.
installation/import/backend failure before a route is clear	references/troubleshooting.md	Covers shared CUDA, dependency, model-access, and optional-extra problems.
license, commercial-use constraints, or citation	references/license-and-citation.md	Distills the repo's license/citation section.

Core operating facts

• Released model ids include baichuan-inc/Baichuan2-7B-Base, baichuan-inc/Baichuan2-7B-Chat, baichuan-inc/Baichuan2-13B-Base, baichuan-inc/Baichuan2-13B-Chat, and published 4-bit Chat variants.
• Chat checkpoints expose model.chat(tokenizer, messages) through Hugging Face trust_remote_code=True. Base checkpoints use model.generate(...) instead.
• The repository demos are GPU-oriented by default. CPU loading is a separate float32 deployment branch and is expected to be slow.
• Quantization and training are CUDA-sensitive. Do not use a CPU import check as proof that those workflows work.
• The bundled scripts intentionally include --help, --dry-run, or validation modes so future agents can inspect plans without downloading large weights.

Do not do this

• Do not tell the user to open or run original checkout scripts; use the bundled helpers and references in this skill.
• Do not force Base checkpoints through Chat-only CLI/web/API wrappers.
• Do not add device_map="auto" to online quantization; the deployment sub-skill explains why.
• Do not launch DeepSpeed training until the data validator and dry-run plan pass.
• Do not import this skill into live repo-skill storage unless a later verification/import workflow is explicitly approved. This construction run was requested as not import.