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LEAP Finetune (leap-finetune) is Liquid’s repo for the full model customization loop: data preparation, training, evals, checkpointing, and export for Liquid Foundation Models (LFMs). Use it when you want a repo-driven workflow that Claude Code or Codex can operate end to end from intent to runnable configs, tests, launches, and follow-up evals.

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What it includes

AreaSupport
TrainingSFT, DPO, GRPO, VLM SFT, VLM DPO, VLM GRPO, MoE SFT, MoE DPO, LoRA, and full fine-tuning
EvalsTraining-time benchmark suites, standalone evals, HF/vLLM backends, and async sidecar or reserved eval workers
Data and rewardsDataset loading, format validation, tool-calling data, VLM image roots, GRPO rewards, judge rewards, and OpenEnv-style RL environments
Launch and exportLocal Ray, SLURM, Modal, KubeRay, checkpoint resume, Hugging Face export, and GGUF export

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Agent quickstart

Clone the repo and install the locked environment: 
git clone https://github.com/Liquid4All/leap-finetune.git
cd leap-finetune
uv sync
For AMD/ROCm machines, sync the ROCm dependency group instead: 
uv sync --no-group cuda --group rocm
Start Claude Code or Codex from the repo root: 
claude
# or
codex
The repo includes AGENTS.md, CLAUDE.md, and mirrored skills under .agents/skills/ and .claude/skills/. Those files help the agent choose the right workflow for data prep, training configs, eval suites, rewards, backend launch, checkpoint inspection, and export. Good starter prompts:
  • “Train LFM2-1.2B with SFT LoRA on the Hugging Face HuggingFaceTB/smoltalk dataset, sweep learning rate and LoRA rank, and tell me which run looks best.”
  • “Fine-tune an LFM vision model on a chart question-answering dataset from Hugging Face, add a small eval suite, and show me the launch command.”
  • “Evaluate my latest support_sft checkpoint with vLLM, compare it against the base model, and write the metrics to results.json.”
  • “Set up a GRPO experiment that rewards valid JSON answers, run a small smoke test, and suggest the next config to try.”

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CLI usage

Run from the repo environment when you are developing configs or changing LFT itself: 
uv run leap-finetune job_configs/sft_example.yaml
uv run leap-finetune run job_configs/sft_example.yaml
uv run leap-finetune eval job_configs/eval_standalone_example.yaml --output results.json
uv run leap-finetune slurm job_configs/sft_example_with_slurm.yaml --output-dir job_configs/slurms
For repeated use from any directory, install the CLI as a uv tool: 
uv tool install git+https://github.com/Liquid4All/leap-finetune.git
leap-finetune /absolute/path/to/config.yaml
To import leap_finetune from another uv project, add it as a dependency: 
uv add git+https://github.com/Liquid4All/leap-finetune.git

# While iterating on a local checkout:
uv add --editable ../leap-finetune

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Python usage

run_config accepts YAML paths or typed config objects. A config with slurm, modal, or kuberay submits to that backend; otherwise local training expects visible CUDA devices. 
from leap_finetune import run_config
from leap_finetune.config import DatasetConfig, JobConfig, PeftConfig, TrainingConfig

job = JobConfig(
    project_name="support_sft",
    model_name="LFM2-1.2B",
    training_type="sft",
    dataset=DatasetConfig(
        path="HuggingFaceTB/smoltalk",
        type="sft",
        subset="all",
        limit=1000,
        test_size=0.2,
    ),
    training_config=TrainingConfig(
        extends="DEFAULT_SFT",
        num_train_epochs=3,
        per_device_train_batch_size=2,
        learning_rate=2e-5,
    ),
    peft_config=PeftConfig(extends="DEFAULT_LORA", use_peft=True),
)

run_config(job)

from leap_finetune import run_config
from leap_finetune.config import (
    DatasetConfig,
    EvalConfig,
    EvalSuiteConfig,
    JobConfig,
    PeftConfig,
    TrainingConfig,
)

job = JobConfig(
    project_name="support_sft_with_evals",
    model_name="LFM2-1.2B",
    training_type="sft",
    dataset=DatasetConfig(
        path="/data/support_train.jsonl",
        type="sft",
        test_size=0.1,
    ),
    training_config=TrainingConfig(
        extends="DEFAULT_SFT",
        num_train_epochs=3,
        per_device_train_batch_size=2,
        learning_rate=2e-5,
        eval_strategy="steps",
        eval_steps=200,
    ),
    peft_config=PeftConfig(extends="DEFAULT_LORA", use_peft=True),
    evals=EvalSuiteConfig(
        max_new_tokens=128,
        benchmarks=[
            EvalConfig(
                name="support_qa",
                path="/data/support_eval.jsonl",
                metric="short_answer",
            ),
        ],
    ),
)

run_config(job)
Eval-only runs use EvalRunConfig, not JobConfig, because they do not include a training dataset or training settings.
from leap_finetune import run_config
from leap_finetune.config import (
    EvalBackendConfig,
    EvalConfig,
    EvalRunConfig,
    EvalSuiteConfig,
)

eval_job = EvalRunConfig(
    project_name="support_eval",
    checkpoint="/models/support_sft/checkpoint-900",
    evals=EvalSuiteConfig(
        max_new_tokens=128,
        benchmarks=[
            EvalConfig(
                name="support_qa",
                path="/data/support_eval.jsonl",
                metric="short_answer",
            ),
        ],
    ),
    backend=EvalBackendConfig(
        type="vllm",
        tensor_parallel_size=1,
        gpu_memory_utilization=0.9,
    ),
)

metrics = run_config(eval_job, output_path="results.json")
print(metrics)
Evaluation data uses the same messages-style format as training data. For generation metrics such as short_answer, the final assistant turn is treated as the ground truth.