LocalAI/docs/content/features/fine-tuning.md

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LocalAI supports fine-tuning LLMs directly through the API and Web UI. Fine-tuning is powered by pluggable backends that implement a generic gRPC interface, allowing support for different training frameworks and model types.

Supported Backends

Backend	Domain	GPU Required	Training Methods	Adapter Types
trl	LLM fine-tuning	No (CPU or GPU)	SFT, DPO, GRPO, RLOO, Reward, KTO, ORPO	LoRA, Full

Availability

Fine-tuning is always enabled. When authentication is enabled, fine-tuning is a per-user feature (default OFF). Admins can enable it for specific users via the user management API.

{{% notice note %}} This feature is experimental and may change in future releases. {{% /notice %}}

Quick Start

1. Start a fine-tuning job

curl -X POST http://localhost:8080/api/fine-tuning/jobs \
  -H "Content-Type: application/json" \
  -d '{
    "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    "backend": "trl",
    "training_method": "sft",
    "training_type": "lora",
    "dataset_source": "yahma/alpaca-cleaned",
    "num_epochs": 1,
    "batch_size": 2,
    "learning_rate": 0.0002,
    "adapter_rank": 16,
    "adapter_alpha": 16,
    "extra_options": {
      "max_seq_length": "512"
    }
  }'

2. Monitor progress (SSE stream)

curl -N http://localhost:8080/api/fine-tuning/jobs/{job_id}/progress

3. List checkpoints

curl http://localhost:8080/api/fine-tuning/jobs/{job_id}/checkpoints

4. Export model

curl -X POST http://localhost:8080/api/fine-tuning/jobs/{job_id}/export \
  -H "Content-Type: application/json" \
  -d '{
    "export_format": "gguf",
    "quantization_method": "q4_k_m",
    "output_path": "/models/my-finetuned-model"
  }'

API Reference

Endpoints

Method	Path	Description
POST	/api/fine-tuning/jobs	Start a fine-tuning job
GET	/api/fine-tuning/jobs	List all jobs
GET	/api/fine-tuning/jobs/:id	Get job details
DELETE	/api/fine-tuning/jobs/:id	Stop a running job
GET	/api/fine-tuning/jobs/:id/progress	SSE progress stream
GET	/api/fine-tuning/jobs/:id/checkpoints	List checkpoints
POST	/api/fine-tuning/jobs/:id/export	Export model
POST	/api/fine-tuning/datasets	Upload dataset file

Job Request Fields

Field	Type	Description
model	string	HuggingFace model ID or local path (required)
backend	string	Backend name (default: trl)
training_method	string	sft, dpo, grpo, rloo, reward, kto, orpo
training_type	string	lora or full
dataset_source	string	HuggingFace dataset ID or local file path (required)
adapter_rank	int	LoRA rank (default: 16)
adapter_alpha	int	LoRA alpha (default: 16)
num_epochs	int	Number of training epochs (default: 3)
batch_size	int	Per-device batch size (default: 2)
learning_rate	float	Learning rate (default: 2e-4)
gradient_accumulation_steps	int	Gradient accumulation (default: 4)
warmup_steps	int	Warmup steps (default: 5)
optimizer	string	adamw_torch, adamw_8bit, sgd, adafactor, prodigy
extra_options	map	Backend-specific options (see below)

Backend-Specific Options (extra_options)

TRL

Key	Description	Default
max_seq_length	Maximum sequence length	512
packing	Enable sequence packing	false
trust_remote_code	Trust remote code in model	false
load_in_4bit	Enable 4-bit quantization (GPU only)	false

DPO-specific (training_method=dpo)

Key	Description	Default
beta	KL penalty coefficient	0.1
loss_type	Loss type: sigmoid, hinge, ipo	sigmoid
max_length	Maximum sequence length	512

GRPO-specific (training_method=grpo)

Key	Description	Default
num_generations	Number of generations per prompt	4
max_completion_length	Max completion token length	256

GRPO Reward Functions

GRPO training requires reward functions to evaluate model completions. Specify them via the reward_functions field (a typed array) or via extra_options["reward_funcs"] (a JSON string).

Built-in Reward Functions

Name	Description	Parameters
format_reward	Checks <think>...</think> then answer format (1.0/0.0)	-
reasoning_accuracy_reward	Extracts <answer> content, compares to dataset's answer column	-
length_reward	Score based on proximity to target length [0, 1]	target_length (default: 200)
xml_tag_reward	Scores properly opened/closed <think> and <answer> tags	-
no_repetition_reward	Penalizes n-gram repetition [0, 1]	-
code_execution_reward	Checks Python code block syntax validity (1.0/0.0)	-

Inline Custom Reward Functions

You can provide custom reward function code as a Python function body. The function receives completions (list of strings) and **kwargs, and must return list[float].

{{% notice warning %}} Inline reward code executes arbitrary Python and is disabled by default.

Inline code is compiled and run in the backend process. The restricted-builtins allowlist is not a security sandbox — trivial expressions can escape it to reach the host (arbitrary code execution). Because the fine-tuning endpoint is unauthenticated by default, inline reward functions are refused unless the operator explicitly opts in by setting LOCALAI_TRL_ALLOW_INLINE_REWARD=true on the backend.

Only enable it on a trusted, access-controlled instance where every caller of the fine-tuning API is authorized to run code on the host. Otherwise use the builtin reward functions above. {{% /notice %}}

The function is given the reduced builtin set (len, int, float, str, list, dict, range, enumerate, zip, map, filter, sorted, min, max, sum, abs, round, any, all, isinstance, print) plus the re, math, json, and string modules, and is compiled and validated at job start (fail-fast on syntax errors). Treat these as ergonomics, not as an isolation boundary.

Example API Request

curl -X POST http://localhost:8080/api/fine-tuning/jobs \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-1.5B-Instruct",
    "backend": "trl",
    "training_method": "grpo",
    "training_type": "lora",
    "dataset_source": "my-reasoning-dataset",
    "num_epochs": 1,
    "batch_size": 2,
    "learning_rate": 5e-6,
    "reward_functions": [
      {"type": "builtin", "name": "reasoning_accuracy_reward"},
      {"type": "builtin", "name": "format_reward"},
      {"type": "builtin", "name": "length_reward", "params": {"target_length": "200"}},
      {"type": "inline", "name": "think_presence", "code": "return [1.0 if \"<think>\" in c else 0.0 for c in completions]"}
    ],
    "extra_options": {
      "num_generations": "4",
      "max_completion_length": "256"
    }
  }'

Export Formats

Format	Description	Notes
lora	LoRA adapter files	Smallest, requires base model
merged_16bit	Full model in 16-bit	Large but standalone
merged_4bit	Full model in 4-bit	Smaller, standalone
gguf	GGUF format	For llama.cpp, requires quantization_method

GGUF Quantization Methods

q4_k_m, q5_k_m, q8_0, f16, q4_0, q5_0

Web UI

Build → Fine-Tune is one page that follows the job from set-up to result. A line at the top shows where you are: set up, check, run, result.

1. Set up - Choose the base model, the dataset (a Hugging Face id or an uploaded file), the kind of training (an adapter, or the full model) and the epochs, batch size and learning rate. Method, backend, adapter settings, optimizer, evaluation, reward functions (GRPO) and extra options are under More options.
2. Check before you start - A list of what is known before the job starts, redrawn as you change the form: whether the model and dataset are set, whether a fine-tuning backend is installed, how much GPU memory (or RAM, when there is no GPU) is free, how much space is free on the models disk, and whether a token is set. LocalAI does not estimate how much memory a job needs or how long it takes, because the server cannot know that before the job starts, so the page says so. A missing model or dataset blocks the start. A warning, such as a missing backend or no GPU, does not.
3. Run - Percent, step, epoch, the server's time estimate and tokens per second, the stages the job passes through, a chart of loss (with the evaluation loss as hollow dots), learning rate and gradient norm, and a log of what the job reported since the page opened. Stop asks whether to keep a checkpoint.
4. Result - A failed job shows the server's message. When the message says memory ran out, the page offers two changes (a batch size of 1 and gradient checkpointing) that are applied to a copy of the setup and start nothing until you press Start. A finished job lists its checkpoints and exports the result as a model, then links to a chat with it and to Models.

Earlier jobs are listed below the form. Reuse puts a job's setup back in the form.

A user needs the fine-tuning permission. Without it, the page says the account cannot fine-tune and who can change that.

Fine-tuning is an add-on

The Fine-Tune page is the fine-tuning add-on. It does not ship with LocalAI. An administrator installs it from Browse add-ons in the drawer and starts it. Once it runs, it opens from the drawer at /app/addons/fine-tuning/jobs and can be pinned. The Build hub no longer has a Fine-Tune tab.

The old link /app/fine-tune keeps working. It opens the add-on page while the add-on is installed and usable, and it opens the same page in the core web UI otherwise.

The API does not change. The page calls /api/fine-tuning/* with the signed-in user's own session, and the server checks the fine-tuning permission on every call, so the add-on never widens what a user may do.

The add-on also offers tools to the chat: list jobs, propose a job, store a small dataset, read the status and the checkpoints of a job, stop a job and export a checkpoint as a model. A tool can only control a job that a tool started. A job started in the page is listed, but a tool cannot stop or export it. A tool never starts, stops or exports anything by itself: it proposes the action and a person confirms it in an approval card. A model or a dataset given as a local path is refused. The tools call the engine through the host operations (see Host operations), which the host setting LOCALAI_ADDON_HOST_OPS grants to this add-on by the finetune. prefix, and they run with the permissions of the person in the chat.

Dataset Formats

Datasets should follow standard HuggingFace formats:

• SFT: Alpaca format (instruction, input, output fields) or ChatML/ShareGPT
• DPO: Preference pairs (prompt, chosen, rejected fields)
• GRPO: Prompts with reward signals

Supported file formats: .json, .jsonl, .csv

Architecture

Fine-tuning uses the same gRPC backend architecture as inference:

1. Proto layer: FineTuneRequest, FineTuneProgress (streaming), StopFineTune, ListCheckpoints, ExportModel
2. Python backends: Each backend implements the gRPC interface with its specific training framework
3. Go service: Manages job lifecycle, routes API requests to backends
4. REST API: HTTP endpoints with SSE progress streaming
5. React UI: Configuration form, real-time training monitor, export panel