---

name: lmflow description: "Routes LMFlow dataset, training, inference, evaluation, alignment, and multimodal workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LMFlow

Use this skill for the LMFlow package when the task is about datasets, fine-tuning, inference, evaluation, post-training alignment, or optional multimodal extensions.

Start Here

• Read references/repo-provenance.md when you need to compare this skill against the current checkout or decide whether it should be refreshed.
• Read references/installation-and-environment.md for install variants, optional extras, and backend split guidance.
• Read references/api-map.md for the core dataclasses, package entry points, and route map.
• Run scripts/check_lmflow_environment.py when you need a quick import and backend smoke check.

Minimal Install

From the repository root, the base editable install is:

python -m pip install -e .

Base install covers LMFlow import, dataset utilities, full/LoRA/LISA fine-tuning, and Hugging Face-style inference/evaluation. Add only the extras needed for the selected workflow, for example:

• pip install -e ".[vllm]" for vLLM inference
• pip install -e ".[sglang]" for SGLang inference
• pip install -e ".[trl]" for DPO/DPOv2/iterative DPO
• pip install -e ".[deepspeed]" for DeepSpeed-backed workflows
• pip install -e ".[ray]" for reward-model inference and Ray-backed paths
• pip install -e ".[multimodal]" for image/text workflows
• pip install -e ".[gradio]" or pip install -e ".[flask]" for UI/service helpers

vLLM and SGLang have incompatible dependency stacks and should live in separate environments.

Minimal Import Check

python -c "import lmflow; print(lmflow.__version__)"

If that works, you can route to a focused sub-skill for the workflow.

Route Map

data-and-templates

Use this for dataset JSON schemas, Dataset methods, conversation templates, validation, save/split/sample operations, and template customization.

training-and-optimization

Use this for full fine-tuning, LoRA, QLoRA, LISA, custom optimizers, and command construction for the training launchers.

inference-and-evaluation

Use this for non-training generation, chat/tool/speculative inference, evaluation, benchmarking, result handling, and optional vLLM/SGLang engines.

post-training-alignment

Use this for reward modeling, reward-model inference, DPO, DPOv2, iterative DPO, RAFT, and LoRA merge workflows.

multimodal-and-extensions

Use this for image/text data, visual chatbots, multimodal fine-tuning, and bounded extension notes such as tool finetuning or long-context templates.

What Not To Expect From The Root

• Do not expect model-specific training recipes here.
• Do not expect engine-specific inference flags here.
• Do not expect dataset schema details here beyond the route map.
• Do not expect alignment algorithm depth here beyond the route map.
• Do not expect extension-specific guidance here beyond the route map.

Shared Troubleshooting

Read references/troubleshooting.md for cross-cutting install/import issues, optional dependency pitfalls, data/config mistakes, CUDA availability, and output-path problems.