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name: moss description: "Routes OpenMOSS/MOSS workflows for local LLM inference, model runtime checks, API or UI serving, and SFT data/fine-tuning preparation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MOSS repo skill

Use this skill for OpenMOSS/MOSS tasks involving the MOSS conversational language model release: model/runtime readiness, local chat inference, FastAPI/Gradio or Streamlit serving, and supervised fine-tuning data preparation. The generated helpers are self-contained and dry-run by default; heavyweight checkpoint loading, service launch, or training must be an explicit task decision.

When to read

• The task names MOSS, OpenMOSS, moss-moon-003, MOSS tokenizer/model classes, INT4/INT8 MOSS checkpoints, MOSS prompt markers, or MOSS plugin transcripts.
• You need to choose a checkpoint precision or GPU plan for MOSS inference.
• You need to validate MOSS environment imports or CUDA availability without downloading model weights.
• You need a safe FastAPI request payload, serving plan, or UI deployment recipe.
• You need to validate MOSS SFT JSON/JSONL conversations or plan a DeepSpeed SFT launch.

First decisions

1. Workflow type — route to a sub-skill below. Do not collapse serving, inference, and training data into one generic recipe.
2. Checkpoint/runtime cost — distinguish dry-run planning from real model execution. Full MOSS generation can download large Hugging Face checkpoints and allocate tens of GB of GPU memory.
3. Precision and device — FP16 supports model parallelism; INT4/INT8 are documented as single-GPU only.
4. Data/license constraints — code, model weights, and data have separate licenses. Check constraints before redistribution, external serving, or commercial data use.

Sub-skill routes

Task	Read
Import/check MossConfig, MossTokenizer, MossForCausalLM; inspect model defaults; reason about quantization, CUDA, Triton, checkpoint families, memory.	sub-skills/model-runtime/SKILL.md
Build MOSS prompts, plan chat generation, validate model/GPU choices, use a dry-run-first generation template, or reason about optional Jittor generation.	sub-skills/inference/SKILL.md
Build API request payloads, plan FastAPI/Gradio/Streamlit service launch, handle uid history and UI parameters, debug service startup.	sub-skills/serving/SKILL.md
Validate MOSS SFT records, plugin/no-plugin transcripts, no-loss spans, Accelerate/DeepSpeed config, and fine-tuning command plans.	sub-skills/fine-tuning-data/SKILL.md

Shared references

• references/repo-provenance.md — source commit and evidence baseline; read before deciding whether this skill is stale.
• references/install-and-dependencies.md — Python/dependency, CUDA, optional UI/training/Jittor guidance.
• references/model-overview.md — checkpoint catalog, memory table, backend and license constraints.
• references/prompt-format.md — canonical MOSS meta instruction, turn markers, plugin sections, and tool APIs.
• references/api-reference.md — verified public class/function signatures and runtime facts.
• references/cli-reference.md — bundled helper commands and safe-versus-heavy execution notes.
• references/serving-and-ui.md — API/UI request, response, and state behavior.
• references/fine-tuning-data.md — SFT data schema and training-plan summary.
• references/troubleshooting.md — cross-cutting install, checkpoint, CUDA, service, and data failures.

Shared safe helper

Run scripts/check_moss_env.py to check imports and optional CUDA availability without loading MOSS checkpoints:

python path/to/moss/scripts/check_moss_env.py --repo-root /path/to/MOSS --json
python path/to/moss/scripts/check_moss_env.py --repo-root /path/to/MOSS --require-cuda --json

The --repo-root argument is supplied by the user for their own checkout. Do not hard-code the checkout that was used to create this skill.

Non-negotiable operating constraints

• Do not claim full MOSS inference, serving, or fine-tuning works unless that heavyweight path was actually run in the target environment.
• Do not use a CPU import check as proof of CUDA generation, quantized Triton kernels, or multi-GPU training.
• Do not recommend model parallelism for quantized INT4/INT8 MOSS checkpoints.
• Do not tell a future agent to rely on original repository scripts; use the bundled helpers and references in this generated skill tree.
• Keep model downloads, public network exposure, training writes, and license implications explicit before executing.