lfm-trainer 0.14.0

Fine-tune LFM 2.5 1.2B for coding tasks on Kaggle multi-GPU with auto-publish to Hugging Face

LFM Trainer

Fine-tune Liquid LFM 2.5 1.2B for coding tasks on Kaggle multi-GPU — with automatic checkpoint publishing to Hugging Face on errors, and post-training GGUF + MLX quantization.

Features

• 🚀 Multi-GPU training via HuggingFace Accelerate / DDP (Kaggle P100 / 2×T4)
• 🧠 LoRA / PEFT for memory-efficient fine-tuning
• 📦 Structured dataset loading — CSV, Parquet, JSONL, HuggingFace Hub IDs, or direct pd.DataFrame objects
• 🔍 Auto-format detection — Alpaca, prompt/response, conversational/chat (DataClaw), single text column
• �️ Tool calling support — LFM 2.5 native <|tool_call_start|> / <|tool_call_end|> tokens; handles OpenAI and DataClaw tool call formats
• �🛡️ Error-resilient training — auto-publishes versioned checkpoints on OOM, SIGTERM (Kaggle timeout), KeyboardInterrupt, or any exception
• 🔑 Flexible HF auth — CLI arg, HF_TOKEN env var, or Kaggle Secrets
• 📐 GGUF export — Q4_K_M, Q6_K, Q8_0 via llama.cpp
• 🍎 MLX export — 4-bit, 6-bit, 8-bit via mlx-lm
• 🏷️ Shared versioning — base model + all quantized variants tagged with the same version
• 🔓 Full fine-tuning — train all parameters (no LoRA) for maximum quality
• 📊 Auto-benchmarking — HumanEval, MBPP, MultiPL-E, BigCodeBench, EvalPlus, Tool Calling, GSM8K, ARC
• 🧠 Reasoning (<think> tags) — train models that think before acting, with <think>...</think> traces
• 🧹 Data quality filters — auto-remove duplicates, empty rows, and length outliers
• 📈 Eval split — hold out a % for validation loss tracking during training
• 📝 Auto model card — generates a HuggingFace README.md with config, benchmarks, and hardware
• 📉 W&B / TensorBoard — optional training metric logging
• 🎯 DPO / PPO / GRPO alignment — preference tuning after SFT via DPO, classic RLHF (PPO), or DeepSeek-style GRPO
• 📚 Continued Pre-Training (CPT) — train on raw text (books, PDFs, code) to inject domain knowledge
• 🔗 LoRA adapter merging — combine multiple adapters into a single model with weighted blending
• 🔍 Auto HP search — try multiple learning rates and pick the best based on eval loss
• ⚡ DeepSpeed ZeRO — ZeRO-2 and ZeRO-3 for multi-GPU training (optimizer + gradient + weight sharding)
• 🎯 Model Distillation — compress a large teacher into a smaller student via KL-divergence
• 📋 Structured Output — train models to generate valid JSON conforming to schemas

Installation

pip install lfm-trainer

Quick Start (Kaggle Notebook)

# Cell 1: Install
!pip install lfm-trainer

# Cell 2: Train on a coding dataset with GGUF export
!lfm-train \
    --dataset peteromallet/dataclaw-peteromallet \
    --hub-repo your-username/lfm-code \
    --export-gguf

# Train on multiple datasets
!lfm-train \
    --dataset code_data.csv \
    --dataset more_code.parquet \
    --dataset sahil2801/CodeAlpaca-20k \
    --hub-repo your-username/lfm-code \
    --epochs 3 \
    --batch-size 2 \
    --export-gguf

The HF token is automatically picked up from Kaggle Secrets (key: HF_TOKEN).

Python API

import pandas as pd
from lfm_trainer import run_training, load_datasets
from lfm_trainer.config import TrainingConfig

# Load multiple sources including DataFrames
df = pd.read_csv("my_code_data.csv")
dataset = load_datasets([
    df,                                    # Direct DataFrame
    "code_data.parquet",                   # Local file
    "peteromallet/dataclaw-peteromallet",   # HuggingFace Hub (conversational)
])

# Or use the full pipeline
cfg = TrainingConfig(
    model_name="liquid/LFM2.5-1.2B-Base",
    dataset_paths=["peteromallet/dataclaw-peteromallet"],
    hub_repo_id="your-username/lfm-code",
    quality_filter=True,
    eval_split=0.1,
    run_benchmark=True,
    benchmark_before_after=True,
    export_gguf=True,
)
run_training(cfg)

CLI Reference

lfm-train --help

Flag	Default	Description
--dataset	(required)	Dataset path or Hub ID (repeatable)
--model	liquid/LFM2.5-1.2B-Base	Model to fine-tune
--resume-from	—	Path or Hub ID of a prior adapter for continual training
--tool-calling-only	off	Keep only samples with tool calls
--quality-filter	off	Remove empty rows, dupes, length outliers
--eval-split	0.0	Hold out a fraction for eval (e.g. 0.1 = 10%)
--hf-token	auto-detect	HuggingFace token
--hub-repo	auto	Hub repo to push to
--no-push	off	Save locally only, skip Hub push
--epochs	3	Training epochs
--batch-size	2	Per-device batch size
--lr	2e-4	Learning rate
--max-seq-length	2048	Max sequence length
--lora-r	16	LoRA rank
--lora-alpha	32	LoRA alpha
--bf16	off	Use bfloat16
--full-finetune	off	Train all params (no LoRA), needs more VRAM
--report-to	none	none, wandb, or tensorboard
--benchmark	off	Run HumanEval + MBPP after training
--benchmark-compare	off	Also benchmark base model for delta
--benchmark-max	all	Cap problems for quick testing
--no-model-card	off	Skip auto model card generation
--export-gguf	off	Export GGUF (Q4_K_M, Q6_K, Q8_0)
--export-mlx	off	Export MLX (4/6/8-bit)
--export-dir	./lfm-exports	Export scratch directory
--alignment-method	dpo	Alignment method: dpo, ppo, or grpo
--alignment-dataset	none	HF dataset for alignment (DPO: chosen/rejected; PPO/GRPO: prompts)
--dpo-beta	0.1	DPO β — higher = more conservative
--reward-model	none	HF reward model for PPO
--grpo-generations	4	Completions per prompt for GRPO
--cpt-sources	none	Raw text sources for CPT (files, dirs, HF datasets)
--cpt-chunk-size	2048	Characters per chunk for CPT
--enable-reasoning	off	Enable <think> reasoning tags in training data
--reasoning-dataset	none	HF dataset for reasoning (e.g., LLM360/TxT360-3efforts)
--reasoning-max-samples	100000	Max samples from reasoning dataset
--auto-hp-search	off	Run auto hyperparameter search before training
--hp-trial-steps	50	Steps per HP search trial
--deepspeed	none	DeepSpeed config: zero2, zero3, or path to JSON
--distill-teacher	none	HF model ID of teacher for knowledge distillation
--distill-temperature	2.0	Distillation softmax temperature
--distill-alpha	0.5	Blend factor: 0=CE only, 1=KL only
--structured-output	off	Mix in JSON schema training data for structured output
--merge-adapters	none	Merge multiple LoRA adapters (skip training)
--merge-output	./lfm-merged	Output dir for merged model
--simulate-error	off	Test auto-publish mechanism

Supported Dataset Formats

The data loader auto-detects column layouts:

Format	Detected By	Example
Alpaca	instruction + output columns	iamtarun/python_code_instructions_18k_alpaca
Prompt/Response	prompt + response columns	Generic Q&A datasets
Conversational	messages column (with tool calls)	peteromallet/dataclaw-peteromallet
Text	Single text column	jdaddyalbs/playwright-mcp-toolcalling
DataFrame	Direct pd.DataFrame objects	In-memory data

Tool-Calling Training

Train exclusively on tool-calling examples using --tool-calling-only:

lfm-train \
    --dataset jdaddyalbs/playwright-mcp-toolcalling \
    --tool-calling-only \
    --hub-repo your-username/lfm-tools \
    --max-seq-length 4096

The filter keeps only samples containing tool call patterns (<|tool_call_start|>, tool_calls, function_call, etc.). Works with any dataset — pre-formatted or auto-formatted.

Continual Training

Train iteratively across multiple datasets, saving locally between rounds:

# Round 1: Coding skills → save locally
lfm-train --dataset sahil2801/CodeAlpaca-20k --no-push

# Round 2: Add tool-calling on top → save locally
lfm-train \
    --resume-from ./lfm-checkpoints/final-adapter \
    --dataset jdaddyalbs/playwright-mcp-toolcalling \
    --tool-calling-only \
    --output-dir ./lfm-checkpoints-r2 \
    --no-push

# Round 3: Final round → push to Hub + export
lfm-train \
    --resume-from ./lfm-checkpoints-r2/final-adapter \
    --dataset peteromallet/dataclaw-peteromallet \
    --hub-repo your-username/lfm-final \
    --export-gguf

Each --resume-from loads the prior adapter and continues learning from where it left off.

How Auto-Publish Works

Training is wrapped in an error handler inspired by Unsloth:

1. SIGTERM (Kaggle timeout) → saves + pushes immediately, then exits
2. CUDA OOM → clears cache, saves + pushes
3. KeyboardInterrupt → saves + pushes
4. Any Exception → saves + pushes, then re-raises

Each checkpoint is tagged with a UTC timestamp (e.g., v20260302-153000) so versions never collide.

Post-Training Export

When --export-gguf or --export-mlx is enabled:

1. LoRA adapters are merged into the base model
2. GGUF: Converted via llama.cpp → Q4_K_M, Q6_K, Q8_0 → pushed to {repo}-GGUF
3. MLX: Converted via mlx-lm → 4-bit, 6-bit, 8-bit → pushed to {repo}-MLX-{N}bit
4. All repos (base + quants) share the same version tag

Note: MLX export requires Apple Silicon. Use --export-gguf on Kaggle (Linux), and --export-mlx locally on Mac.

Examples

See the examples/ directory for ready-to-run scripts:

📁 getting_started/ — First steps

Example	Description
basic_training.py	Simple Alpaca coding fine-tune
kaggle_notebook.py	Copy-paste Kaggle cells
wandb_training.py	W&B logging with auto API key detection
full_finetune.py	Full parameter training (no LoRA)
continual_training.py	Multi-round training with local saves
multi_dataset_training.py	Combining Hub + local + DataFrame sources
tool_calling_training.py	Tool-calling-only with playwright MCP
auto_hp_search.py	Auto learning rate search before training

📁 training_modes/ — Advanced training techniques

Example	Description
deepspeed_training.py	DeepSpeed ZeRO-2 and ZeRO-3 multi-GPU
distillation.py	Distill 7B teacher → 1.2B student
dpo_alignment.py	DPO / PPO / GRPO alignment after SFT
merge_adapters.py	Merge multiple LoRA adapters (with weights)
cpt_raw_text.py	Train on books, PDFs, or raw text (CPT)
structured_output.py	JSON schema training + validation

📁 domain_specialists/ — Domain-specific fine-tuning

Example	Description
terminal_agent.py	Terminal agent with Nemotron-Terminal-Corpus
medical_assistant.py	🏥 Healthcare: medical Q&A, patient-doctor
sql_specialist.py	🗄️ Text-to-SQL & data analysis
legal_assistant.py	⚖️ Legal reasoning, contract analysis
finance_assistant.py	📈 Financial analysis, sentiment
cybersecurity.py	🔒 Security analysis, CTF, pentesting

📁 recipes/ — End-to-end model recipes

Example	Description
recipe_tool_calling.py	🍳 Tool calling specialist
recipe_reasoning_tools.py	🍳 Reasoning + tool calling (TxT360)
recipe_from_scratch.py	🍳 Domain expert from books/blogs
chatbot_assistant.py	💬 Multi-turn chat + DPO alignment
api_builder.py	🔧 API dev: REST, OpenAPI, function calling
math_reasoning.py	🧮 Math reasoning with <think> traces
multilang_coding.py	💻 Multi-language coder, code reviewer

📁 advanced/ — Benchmarking & export

Example	Description
benchmark_training.py	Train + HumanEval/MBPP benchmark + auto-upload
full_benchmark_suite.py	All 9 benchmarks with before/after comparison
benchmark_only.py	Evaluate any model without training
export_only.py	Standalone GGUF/MLX quantization

📚 Documentation

New to LLM fine-tuning? Start here — no prerequisites beyond basic Python:

#	Guide	What you'll learn
1	What is an LLM?	Tokens, embeddings, attention, transformers
2	How Training Works	Loss functions, backprop, gradient descent
3	Fine-Tuning vs Scratch	Transfer learning, catastrophic forgetting
4	LoRA Explained	Low-rank adapters, the math, pure Python impl
5	Full Fine-Tuning	Gradient checkpointing, memory management
6	Data Preparation	Formats, tokenization, quality filters
7	Evaluation & Benchmarks	HumanEval, MBPP, pass@k metric
8	Quantization & Export	GGUF, MLX, INT4/INT8
9	Architecture Deep-Dive	How lfm-trainer is built
10	DPO, PPO, GRPO & Alignment	DPO/PPO/GRPO math, datasets, pipeline
11	Continued Pre-Training (CPT)	Train on books, raw text, domain knowledge
12	Reasoning & Thinking	<think> tags, TxT360, model recipes, benchmarks
13	DeepSpeed & Distillation	ZeRO-2/3, knowledge distillation, teacher→student
14	Structured Output	JSON-mode training, schema validation, benchmarks

License

MIT