---

name: litgpt description: "Use LitGPT to run local LLM inference, chat, finetuning, pretraining, checkpoint conversion, evaluation, and LitServe deployment with safe checks and workflow routing." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LitGPT Repo Skill

Use this skill when a user asks about LitGPT, the litgpt CLI, the litgpt Python package, local LLM generation, finetuning, pretraining, checkpoint conversion, evaluation, or serving. This root skill routes tasks to focused sub-skills and keeps long workflows self-contained.

Install And Import Baseline

Public installation patterns:

pip install litgpt
pip install 'litgpt[extra]'

Use the base package for core CLI/API inspection and local checkpoint workflows. Use litgpt[extra] only when the user needs optional surfaces such as evaluation harness support, LitServe, dataset helpers, logging integrations, quantization, or tokenizer/model conversion extras.

Run the bundled environment check before relying on optional routes:

python scripts/check_litgpt_environment.py --json

Minimal import check:

from litgpt import Config, GPT, LLM, Tokenizer
print(Config.from_name("pythia-14m").name)

Route By Task

• sub-skills/inference-chat/: local generation, chat REPLs, Python LLM.load / LLM.generate, prompt styles, sampling, quantized inference, sequential/tensor-parallel generation, and offline generation planning.
• sub-skills/training-data/: finetune_lora, QLoRA, full finetuning, adapter/adapter_v2, pretraining, continued pretraining, JSON/JSONL SFT data, built-in data modules, config recipes, training args, resume/logging, and OOM-safe command construction.
• sub-skills/checkpoint-conversion/: download, validate, HF-to-LitGPT conversion, LitGPT-to-HF conversion, pretrained checkpoint export, LoRA merge, model config lookup, tokenizer/config layout, and checkpoint directory classification.
• sub-skills/evaluation-serving/: evaluate, LM Evaluation Harness task planning, result files, serve, LitServe simple/streaming/OpenAI-compatible endpoints, curl examples, and optional dependency checks.

Common Routing Patterns

• If the user has a model name but no local files, start with sub-skills/checkpoint-conversion/ for litgpt download and format decisions.
• If the user has a local checkpoint and wants text output, start with sub-skills/inference-chat/ after a checkpoint layout check.
• If the user has training data or wants LoRA/QLoRA/full finetuning, start with sub-skills/training-data/, then return to checkpoint conversion for LoRA merge or to inference/evaluation for downstream use.
• If the user asks for benchmarks, lm_eval, HTTP endpoints, OpenAI-compatible requests, or streaming APIs, start with sub-skills/evaluation-serving/.
• If multiple workflows apply, validate checkpoint layout first, validate data second, and only then run expensive model loading, training, evaluation, or serving.

Shared References

• references/capability-map.md: workflow-to-sub-skill routing, optional dependency ownership, and safe native verification candidates.
• references/troubleshooting.md: cross-cutting install/import, optional dependency, hardware, download, CLI config, data, checkpoint, and path failures.
• references/repo-provenance.md: source repository snapshot and refresh baseline for detecting stale skill content.
• references/repo-routing-metadata.json: structured routing metadata consumed by DisCo import tooling.

Shared Script

• scripts/check_litgpt_environment.py: safe environment and optional dependency probe. It imports metadata and optional packages only; it does not download models, load weights, train, evaluate, or start a server.

Safety Defaults

• Treat downloads, checkpoint conversion, training, evaluation, and serving as potentially expensive; ask before running them unless the user has already authorized a bounded run.
• Prefer help commands, bundled checkers, static layout validation, --print_config, tiny fixtures, and dry planning before running GPU/model workflows.
• Never expose access tokens in commands. Prefer environment variables or the user's secured credential mechanism.
• Avoid assuming CUDA, bitsandbytes, lm_eval, LitServe, Thunder, XLA, or logger integrations are installed. Check optional dependencies first.
• Runtime guidance in this skill is self-contained. Do not depend on the original source checkout for docs, tests, examples, or helper scripts.