GitHub - Liquid4All/leap-finetune: A minimal fine-tuning repo for LFM2, fully built on Open Source.

Try LFM - Documentation - LEAP

Setup - Quickstart - CLI - Backends - Datasets - GRPO - Evaluation - GGUF - Contributing

LEAP-Finetune is a minimal fine-tuning repo for LFM2. It handles dataset formatting, validation, distributed orchestration, checkpointing, and export for local GPU nodes, SLURM clusters, Modal, and Kubernetes/KubeRay.

For feature requests or custom infrastructure support, reach out to support@liquid.ai with your setup.

Setup

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Clone the repo:

git clone <repository-url>
cd leap_finetune

CUDA / NVIDIA clusters use the default dependency groups:

uv sync

AMD / ROCm clusters should use the ROCm group instead:

uv sync --no-group cuda --group rocm

The ROCm group is lockfile-managed and uses vLLM's ROCm wheel index for vLLM plus the matching torch, torchvision, torchaudio, flash-attn, and triton stack. The pinned ROCm vLLM wheels are Python 3.12 Linux wheels, so use the repo's .python-version when creating AMD environments.

If flash-attn was built against a different Torch/CUDA ABI, errors such as flash_attn_2_cuda... undefined symbol usually mean the environment needs to be rebuilt:

uv cache clean flash-attn
rm -rf .venv
MAX_JOBS=1 uv sync

Run this on a machine with a CUDA toolkit and enough build memory available if uv needs to rebuild flash-attn from source.

Quickstart

Create a YAML config file or copy one from job_configs/:

project_name: "my_sft_project"
model_name: "LFM2-1.2B"
training_type: "sft"

dataset:
  path: "HuggingFaceTB/smoltalk"
  type: "sft"
  limit: 1000
  test_size: 0.2
  subset: "all"

training_config:
  extends: "DEFAULT_SFT"
  num_train_epochs: 3
  per_device_train_batch_size: 2
  learning_rate: 2e-5

peft_config:
  extends: "DEFAULT_LORA"
  use_peft: true

training_config.extends inherits from a base config such as DEFAULT_SFT, DEFAULT_DPO, or DEFAULT_VLM_SFT; fields in your YAML override the base. peft_config.extends works the same way for LoRA defaults such as DEFAULT_LORA and DEFAULT_VLM_LORA.

Launch training:

uv run leap-finetune job_configs/sft_example.yaml

Training uses Ray Train and Accelerate for distributed execution. Unless output_dir is set, results are written to outputs/{project_name}/{run_name}/. Each run gets a unique name based on the model, dataset, learning rate, and timestamp.

Useful starter configs:

Mode	Config
SFT	`job_configs/sft_example.yaml`
SFT + LoRA	`job_configs/sft_with_lora_example.yaml`
DPO	`job_configs/dpo_example.yaml`
VLM SFT	`job_configs/vlm_sft_example.yaml`
VLM DPO	`job_configs/vlm_dpo_example.yaml`
GRPO	`job_configs/grpo_example.yaml`
VLM GRPO	`job_configs/vlm_grpo_grounding_example.yaml`
MoE SFT	`job_configs/moe_sft_example.yaml`
MoE DPO	`job_configs/moe_dpo_example.yaml`
Expert-parallel MoE SFT	`job_configs/moe_ep_sft_example.yaml`
Standalone eval	`job_configs/eval_standalone_example.yaml`

CLI and Python Usage

During development, prefer the repo environment so you get the lockfile-managed CUDA/vLLM stack:

uv run leap-finetune job_configs/sft_example.yaml
uv run leap-finetune run job_configs/sft_example.yaml
uv run leap-finetune job_configs/eval_standalone_example.yaml
uv run leap-finetune eval job_configs/eval_standalone_example.yaml --output results.json

Install the command as a reusable tool from a checkout:

uv tool install --editable . --force
leap-finetune /absolute/path/to/config.yaml
leap-finetune slurm /absolute/path/to/config.yaml --output-dir /absolute/path/to/slurms
leap-finetune /absolute/path/to/eval_config.yaml
leap-finetune eval /absolute/path/to/eval_config.yaml --output /absolute/path/to/results.json

uv tool install creates an isolated tool environment. Use explicit config paths when invoking the command outside the repo; bare names like sft_example.yaml resolve from the current directory's job_configs/ first, then from the installed package's LEAP_FINETUNE_DIR.

For one-off execution without installing the command:

uvx --from . leap-finetune /absolute/path/to/config.yaml

You can also start a run from Python. This uses the same backend dispatch as the CLI: configs with slurm, modal, or kuberay submit remotely; other configs run local Ray training and require visible CUDA devices.

from leap_finetune import run_config

run_config("/absolute/path/to/config.yaml")

Standalone evals use the same entry point:

from leap_finetune import run_config

metrics = run_config("/absolute/path/to/eval_config.yaml")

Run that file inside an environment where leap-finetune is installed:

uv run --with-editable . python launch_training.py

Execution Backends

Configs without a remote backend section run in the current environment through Ray Train. Add one of the backend blocks below to submit the same config to a remote runtime.

Modal

Modal lets you run training jobs on serverless GPUs from a laptop or Mac. No local GPU is required.

One-time setup:

huggingface-cli login
modal setup

Add a modal: section:

modal:
  gpu: "H100:4"
  timeout: 86400
  output_volume: "leap-finetune"
  output_dir: "/outputs"
  detach: false

Run:

uv run leap-finetune job_configs/sft_example_modal.yaml

In attached mode (detach: false), the CLI builds the container image, auto-creates a huggingface-secret from your local HF token, streams logs, and saves checkpoints to a Modal Volume.

Retrieve checkpoints:

modal volume ls leap-finetune
modal volume get leap-finetune <checkpoint-name> ./local-outputs

Set detach: true to submit and disconnect. The CLI prints the Modal app ID plus commands to monitor or stop it:

modal app logs ap-...
modal app stop ap-...

Use the printed ap-... app ID for detached logs. See job_configs/sft_example_modal.yaml.

SLURM

If your config includes a slurm: section, leap-finetune auto-generates and submits a SLURM script:

uv run leap-finetune job_configs/sft_example_with_slurm.yaml

Generate a SLURM script without submitting it:

uv run leap-finetune slurm <path_to_config.yaml>

Monitor your SLURM jobs in a TUI:

uv run turm --me

Kubernetes / KubeRay

If your config includes a kuberay: section, leap-finetune submits a KubeRay RayJob instead of launching local training. You need a configured Kubernetes context, KubeRay CRDs installed, and a container image that already contains this repo plus its Python environment.

kuberay:
  image: "registry.example.com/leap-finetune:latest"
  namespace: "training"
  worker_replicas: 2
  gpus_per_worker: 4
  output_dir: "/outputs"
  output_pvc: "leap-finetune-outputs"
  env:
    HF_HOME: "/outputs/hf-cache"

Run the same command as local training:

uv run leap-finetune path/to/config.yaml

The CLI creates a ConfigMap for the training config, submits a RayJob, and prints kubectl commands for status and logs. The product of worker_replicas and gpus_per_worker becomes ray.num_workers unless you set it explicitly.

Experiment Tracking

Add tracker to training_config:

training_config:
  tracker: "trackio" # or "wandb"

Trackio logs to a HuggingFace Space. trackio_space_id is auto-created if needed, and Modal injects the HF token automatically:

training_config:
  tracker: "trackio"
  trackio_space_id: "username/my-dashboard"

Weights & Biases uses WANDB_API_KEY:

training_config:
  tracker: "wandb"

Set the key locally or add it as a Modal secret:

export WANDB_API_KEY=your_key

modal secret create wandb-secret WANDB_API_KEY=your_key

modal:
  secrets:
    - "wandb-secret"

Bundle Checkpoints for LEAP

When training is done, bundle your output checkpoint with leap-bundle to use it directly within LEAP. See the LEAP bundle quickstart.

Datasets

Loading Data

The dataset.path field accepts local files, HuggingFace Hub IDs, and cloud storage URIs:

Source	Example `path`
Local file	`/path/to/data.jsonl`, `/path/to/data.parquet`
HuggingFace Hub	`HuggingFaceTB/smoltalk`
S3	`s3://bucket/path/to/data.parquet`
GCS	`gs://bucket/path/to/data.parquet`
Azure	`az://container/path/to/data.parquet`

Cloud storage requires appropriate AWS, GCP, or Azure credentials. Use subset for HuggingFace datasets with multiple configs, split for HF split expressions such as train+validation, and limit to cap samples for quick testing.

SFT

{
  "messages": [
    { "role": "user", "content": "What is the capital of France?" },
    { "role": "assistant", "content": "The capital of France is Paris." }
  ]
}

DPO

DPO uses preference columns instead of a top-level messages field. For single-turn data, prompt, chosen, and rejected can be plain strings:

{
  "prompt": "What is the capital of France?",
  "chosen": "The capital of France is Paris.",
  "rejected": "The capital of France is London."
}

For multi-turn DPO, make prompt the shared conversation history and make chosen / rejected the preferred and rejected assistant completions:

{
  "prompt": [
    { "role": "system", "content": "You are a helpful assistant." },
    { "role": "user", "content": "I am planning a trip to France." },
    { "role": "assistant", "content": "What would you like to know?" },
    { "role": "user", "content": "What is the capital?" }
  ],
  "chosen": [
    { "role": "assistant", "content": "The capital of France is Paris." }
  ],
  "rejected": [
    { "role": "assistant", "content": "The capital of France is London." }
  ]
}

Rows without prompt are also accepted if chosen and rejected are full conversations with the same shared prefix; the tokenizer extracts that prefix as the prompt. Prefer the explicit prompt shape above when writing new data.

VLM SFT

{
  "messages": [
    {
      "role": "system",
      "content": [
        {
          "type": "text",
          "text": "You are an image-based assistant. Answer questions based on the provided image."
        }
      ]
    },
    {
      "role": "user",
      "content": [
        { "type": "image", "image": "/path/to/image.jpg" },
        { "type": "text", "text": "What do you see in this image?" }
      ]
    },
    {
      "role": "assistant",
      "content": [{ "type": "text", "text": "I see a car in the image." }]
    }
  ]
}

VLM datasets often store image paths in a separate column. Merge those image references into each row's messages content before training.

VLM DPO

Use training_type: "vlm_dpo" for multimodal preference data. Each row should provide prompt, chosen, and rejected message lists plus either an image or images column:

{
  "image": "images/chart.png",
  "prompt": [
    {
      "role": "user",
      "content": [{ "type": "text", "text": "Which trend is most important?" }]
    }
  ],
  "chosen": [
    {
      "role": "assistant",
      "content": "Revenue grows fastest in Q4."
    }
  ],
  "rejected": [
    {
      "role": "assistant",
      "content": "The chart is inconclusive."
    }
  ]
}

Use images instead of image for multi-image rows. Relative image paths are resolved against dataset.image_root when set. See job_configs/vlm_dpo_example.yaml for a complete LoRA config. freeze_vision_encoder and optimizer_type: "adamw_8bit" are optional knobs, not defaults.

GRPO and VLM GRPO

GRPO can reuse the SFT/VLM SFT messages format. The loader turns each row into prompt and solution for online reward computation, and any extra columns are forwarded to reward functions. See GRPO for the full dataset, reward, and vLLM rollout contract.

Tool Calling Datasets

Tool calls use LFM bracket notation in the assistant content field. Tool definitions go in the system prompt, and tool responses use role: "tool".

{
  "messages": [
    {
      "role": "system",
      "content": "List of tools: [{\"type\":\"function\",\"function\":{\"name\":\"get_weather\",\"description\":\"Get weather for a city\",\"parameters\":{\"type\":\"object\",\"properties\":{\"location\":{\"type\":\"string\"}},\"required\":[\"location\"]}}}]"
    },
    { "role": "user", "content": "What's the weather in Boston?" },
    {
      "role": "assistant",
      "content": "<|tool_call_start|>[get_weather(location=\"Boston\")]<|tool_call_end|>"
    },
    {
      "role": "tool",
      "content": "{\"temperature\": 72, \"condition\": \"sunny\"}"
    },
    { "role": "assistant", "content": "It's 72 F and sunny in Boston." }
  ]
}

Tool calls must be pre-baked in content using <|tool_call_start|>[func(args)]<|tool_call_end|> bracket notation.
Structured tool_calls fields in OpenAI format are auto-converted when present.
Foreign formats such as <tool_call> XML are rejected with an actionable error.
Do not include <|tool_response_start|> or <|tool_response_end|> markers in role: "tool" messages. The LFM2 chat template adds them during tokenization.
LFM2 models expect <|tool_list_start|> and <|tool_list_end|> around tool definitions in the system prompt. Include them for LFM2 and omit them for LFM2.5. The pipeline warns on mismatches and auto-strips <|tool_list_start|> when training LFM2.5.

Resuming Training

Resume interrupted runs from the last checkpoint with optimizer state, LR schedule, training step, RNG state, and tracker continuity intact:

training_config:
  resume_from_checkpoint: "latest"

latest finds the most recent run directory under outputs/{project_name}/ and resumes from its latest checkpoint. To resume from a specific checkpoint:

training_config:
  resume_from_checkpoint: "/path/to/outputs/my_project/run_name/checkpoint-step-8000"

To resume a run, save_only_model must be False. The wandb run ID is saved to <run_dir>/.wandb_run_id; resumed runs reuse the same wandb run, and fresh runs create a new one.

GRPO

GRPO runs online RL with TRL v1's GRPOTrainer. Use training_type: "grpo" for text models and training_type: "vlm_grpo" for vision-language models. Both modes use the same YAML entrypoint as SFT/DPO, the same Ray Train launcher, and vLLM rollouts by default.

Dataset Contract

Text GRPO can reuse the SFT messages format: the loader builds prompt from non-assistant turns and solution from the last assistant message. Native prompt / solution columns also work. VLM GRPO uses the same multimodal messages shape as VLM SFT. Extra dataset columns are forwarded to reward functions as keyword arguments.

Rewards

The rewards: block resolves plain Python callables and task recipes from rewards/. Shipped primitive functions can be referenced by function name:

rewards:
  funcs:
    - "accuracy_reward"
    - "length_reward"
  weights: [1.0, 0.1]

Task recipes bundle multiple reward functions and their default weights:

rewards:
  recipe: "tasks/vlm_grounding/recipe.py::VLMGroundingIoURecipe"

If you combine recipe: and funcs:, recipe rewards are ordered first. A weights: override must match the expanded reward list. Absolute paths and ./rewards/file.py::function_name specs work for custom rewards.

Judge LLM Reward

Add rewards.judge when the reward signal should come from an LLM grader. Without base_url, the driver starts a local trl vllm-serve judge server before Ray initializes and exports the endpoint to workers:

rewards:
  judge:
    model: "LFM2-1.2B"
    weight: 1.0
    prompt_template: |
      Prompt:
      {prompt}

      Assistant response:
      {completion}

      Reference answer or rubric:
      {solution}

      Return only JSON: {"score": 0.0}

grpo_rollout:
  judge_gpus: 1

For an external judge, set rewards.judge.base_url and omit judge_gpus. Judge scores are parsed from JSON or the first number in the response and normalized from min_score/max_score to [0, 1].

vLLM Rollouts

DEFAULT_GRPO and DEFAULT_VLM_GRPO set use_vllm: true and vllm_mode: "colocate". Colocate mode runs vLLM inside each training worker. Server mode starts trl vllm-serve on driver GPUs before Ray initializes. Configure GPU counts, not device ids:

grpo_rollout:
  server_gpus: 1 # reserve 1 local GPU for vLLM; training gets the rest
  judge_gpus: 1 # optional: reserve 1 local GPU for rewards.judge
  # training_gpus: 3    # or set only this and vLLM gets the remaining GPUs
  tensor_parallel_size: 1
  dtype: "bfloat16"
  gpu_memory_utilization: 0.9

training_config:
  extends: "DEFAULT_GRPO"
  vllm_mode: "server"
  vllm_server_host: "auto"
  vllm_server_port: 8000

Local server partitioning is single-node only. For multi-node GRPO, use colocate mode or point vllm_server_base_url at an externally managed vLLM server without setting server_gpus or training_gpus.

Example configs:

job_configs/grpo_example.yaml: text GRPO quickstart with the GSM8K recipe.
job_configs/grpo_server_mode_example.yaml: text GRPO with a local trl vllm-serve rollout server.
job_configs/vlm_grpo_grounding_example.yaml: VLM GRPO with the visual-grounding recipe.

Launch the same way as SFT/DPO:

uv run leap-finetune job_configs/grpo_example.yaml

Agentic Environments

For tasks where the environment state evolves from agent actions, such as browsing, tool use, game simulators, or stateful multi-turn tasks, leap-finetune supports OpenEnv via an optional rl_env: block:

uv sync --extra rl-env

See src/leap_finetune/rl/environments/README.md. For anything scorable by a pure Python function, prefer the rewards: path; it is simpler and faster.

Evaluation

Run benchmarks during training at every eval_steps by adding an evals: section. The legacy benchmarks: alias still parses.

evals:
  max_new_tokens: 128
  benchmarks:
    - name: "mmmu_val"
      path: "/data/mmmu_val.jsonl"
      metric: "short_answer"

    - name: "imagenette"
      path: "/data/imagenette_eval.jsonl"
      metric: "logprob_zero_shot"

Benchmark data uses the same HF messages schema as training data. Available metrics include short_answer, grounding_iou, mcq_gen, and logprob_zero_shot. Results are logged to wandb at benchmark/{name}/score.

See the Evaluation Guide for data format examples, YAML reference, and custom metrics.

Run the same eval suite without training:

uv run leap-finetune job_configs/eval_standalone_example.yaml

Standalone eval configs use model_name or checkpoint, evals:, and an optional backend: block. They do not include dataset, training_type, training_config, or async_eval. Text evals default to modality: text; set modality: vlm only for standalone VLM evals.

Use the explicit eval subcommand when you want CLI-only eval options such as writing metrics to JSON:

uv run leap-finetune eval job_configs/eval_standalone_example.yaml --output results.json

The same path is available from Python:

from leap_finetune import run_config

metrics = run_config("job_configs/eval_standalone_example.yaml")

Async Eval (vLLM)

By default, every eval_steps blocks training until benchmarks finish. For large generation suites this dominates wall-clock time. Add an async_eval block to run benchmarks without blocking training, using vLLM for the actual generation. Results are logged to wandb with benchmark/step and train/global_step fields so dashboards can align benchmark metrics to the training step that triggered them.

Three modes (default is sync = today's behavior):

Mode	Engine	Pauses training?	GPUs reserved	Latency	Multi-node training	Best for
`sync`	HF transformers	Yes	None	Immediate	✓	Small/fast eval suites; default
`sidecar`	vLLM	No	None (slurm-scheduled per cycle)	Slurm queue + eval time	✓	Tight clusters; eval should be free of training cost
`reserved`	vLLM	No	N throughout the run	~30–60s respawn per cycle	Single-node only	Spare GPUs on one node, want predictable eval latency

reserved mode carves its GPUs off the same SLURM allocation as training via the driver's CUDA_VISIBLE_DEVICES, which only affects the head node. Multi-node training will raise NotImplementedError at startup — use sidecar instead, which scales to any node count.

Async sidecar mode serves generation through vLLM and falls back to an HF model inside the sidecar for benchmark types vLLM cannot serve, such as logprob scoring. Reserved mode keeps a persistent vLLM server and should be used for generation benchmarks; use sync or sidecar for logprob suites.

# Opt in by adding this block. See job_configs/sft_with_async_eval_example.yaml
async_eval:
  mode: sidecar # sync (default) | sidecar | reserved
  vllm_gpus: 1
  tensor_parallel_size: 1
  gpu_memory_utilization: 0.9

  # mode=sidecar: short sbatch job per eval_steps
  sbatch:
    time: "00:30:00"
    # partition / account default to inheriting from the parent job

  # mode=reserved: long-running vllm-serve on dedicated GPUs (single-node only for v1)
  reserved:
    weight_reload: respawn
    server_port: 8100

Failures are isolated: if eval crashes or sbatch is rejected, training continues. After failure.max_consecutive consecutive failures the callback disables itself for the rest of the run. See job_configs/sft_with_async_eval_example.yaml for a full example.

Post-Training Evaluation with lmms-eval

For standard VLM benchmarks such as MMMU, OCRBench, RefCOCO, and POPE, use an environment that includes the private Liquid4All lmms-eval fork with LFM2 model support.

Evaluate a fine-tuned checkpoint:

python -m lmms_eval \
  --model lfm2_vl \
  --model_args pretrained=/path/to/checkpoint \
  --tasks mmmu_val,ocrbench,pope \
  --batch_size 1

Multi-GPU:

torchrun --nproc-per-node=4 -m lmms_eval \
  --model lfm2_vl \
  --model_args pretrained=/path/to/checkpoint \
  --tasks mmmu_val,ocrbench,pope \
  --batch_size 1

For faster evaluation with the vLLM backend:

python -m lmms_eval \
  --model lfm2_vl_vllm \
  --model_args pretrained=/path/to/checkpoint,tensor_parallel_size=1,gpu_memory_utilization=0.85 \
  --tasks mmmu_val,ocrbench,pope \
  --batch_size 64

The lmms-eval and vLLM packages are sourced from private Liquid4All forks with LFM2 model support, so SSH access to those repos is required.

Quantization / GGUF Export

Export a HuggingFace checkpoint or PEFT adapter to GGUF with leap-export-gguf:

uv run leap-export-gguf /path/to/checkpoint --quant F16 --output-dir /lambdafs/gguf

Repeat --quant to produce multiple outputs:

uv run leap-export-gguf /path/to/checkpoint \
  --quant F16 \
  --quant Q4_K_M \
  --output-dir /lambdafs/gguf \
  --llama-cpp-dir /path/to/llama.cpp

F16, BF16, F32, and Q8_0 are exported directly with the bundled llama.cpp conversion scripts. K-quants such as Q4_K_M, Q5_K_M, and Q6_K require a built llama.cpp checkout containing build/bin/llama-quantize; pass --llama-cpp-dir or set LLAMA_CPP_DIR.

PEFT adapter directories can be exported with F16, BF16, F32, or Q8_0:

uv run leap-export-gguf /path/to/adapter \
  --base-model-path /path/to/base-model \
  --quant F16 \
  --output-dir /lambdafs/gguf

For adapter K-quants, merge the adapter into the base model first, then export the merged checkpoint.

Advanced Configuration

Default base configs live in src/leap_finetune/training/default_configs/ and are auto-discovered. New configs added to those files are immediately available via extends in YAML.

Liger Kernel is installed by the default CUDA group. Enable it with use_liger_kernel: true in training_config.

LoRA is configured through peft_config. Continued LoRA training can load an existing adapter with adapter_path; VLM configs can optionally freeze the vision encoder with freeze_vision_encoder and use bitsandbytes 8-bit AdamW with optimizer_type: "adamw_8bit".

Pinned chat templates live in job_configs/chat_templates/. Non-local LiquidAI LFM2.5 and LFM2-24B models select the pinned LFM2.5 template by default.

Contributing

Testing

The test suite is intentionally scoped to four buckets: config parsing, e2e, RL, and MoE. See tests/README.md for the current layout. E2E fixtures and SLURM launchers live under tests/e2e/.

Run the normal tests:

uv run pytest tests/config tests/rl tests/moe -q

GPU e2e tests require an appropriate GPU or cluster backend; see tests/e2e/ for launchers and fixtures.

Pull Requests

Install pre-commit hooks:

uv run pre-commit install

Run hooks manually:

uv run pre-commit run --all-files

Open a PR with a clear description of the behavior changed, tests run, and any known limitations.

Name		Name	Last commit message	Last commit date
Latest commit History 37 Commits
.github/workflows		.github/workflows
job_configs		job_configs
job_datasets		job_datasets
rewards		rewards
src/leap_finetune		src/leap_finetune
tests		tests
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.python-version		.python-version
AGENTS.md		AGENTS.md
README.md		README.md
banner.png		banner.png
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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Setup

Quickstart

CLI and Python Usage

Execution Backends

Modal

SLURM

Kubernetes / KubeRay

Experiment Tracking

Bundle Checkpoints for LEAP

Datasets

Loading Data

SFT

DPO

VLM SFT

VLM DPO

GRPO and VLM GRPO

Tool Calling Datasets

Resuming Training

GRPO

Dataset Contract

Rewards

Judge LLM Reward

vLLM Rollouts

Agentic Environments

Evaluation

Async Eval (vLLM)

Post-Training Evaluation with lmms-eval

Quantization / GGUF Export

Advanced Configuration

Contributing

Testing

Pull Requests

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