README.md

metadata

title: LLM Trainer
emoji: 🏋
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 6.15.2
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
tags:
  - llm
  - lora
  - qlora
  - sft
  - fine-tuning
  - fine-tuning-tools
  - training
  - cpu
  - deslop
  - gradio-theme
short_description: QLoRA SFT training + deslop - CPU

LLM QLoRA SFT Trainer

QLoRA training pipeline for any HuggingFace causal LM. Works on CPU (HF Space) or GPU (local). Uses 4-bit quantization when model is pre-quantized (bnb-4bit). Default model: unsloth/gemma-4-E2B-it.

Steps (each optional)

1. Deslop -- Remove AI slop phrases via FTPO training (auto-antislop-style)
2. SFT QLoRA -- Fine-tune on your chat dataset (TRL SFTTrainer + Adafactor + gradient checkpointing)

Web UI

• Upload a dataset or enter a HuggingFace dataset ID (supports [:N] slicing, e.g. HuggingFaceH4/no_robots[:500])
• Select model from dropdown (auto-populated from unsloth org) or type any model ID
• Auto-detects dataset format: messages, ShareGPT, Alpaca, text
• Check which steps to run, click "Start Training"
• Stop training anytime with the red "Stop Training" button
• Download the QLoRA adapter ZIP
• Chat tab with streaming inference and thinking mode toggle

CLI Usage

pip install -r requirements.txt

# SFT on GPU
python app.py --sft --dataset data.jsonl --device cuda

# SFT on CPU (use 4-bit model for faster training)
python app.py --sft --dataset data.jsonl --device cpu

# Deslop + SFT
python app.py --deslop --sft --dataset data.jsonl

# From HuggingFace dataset
python app.py --sft --hf-dataset HuggingFaceH4/no_robots

# No args = launch Gradio web UI
python app.py

CLI Options

Flag	Default	Description
--model	unsloth/gemma-4-E2B-it-unsloth-bnb-4bit	HuggingFace model ID
--dataset		Path to .jsonl/.csv/.parquet/.txt
--hf-dataset		HuggingFace dataset ID
--device	auto	auto, cpu, or cuda
--deslop		Run deslop FTPO
--sft		Run SFT QLoRA
--epochs	1	Training epochs
--lr	2e-4	Learning rate
--rank	16	LoRA rank
--max-seq	1024	Max sequence length

Performance (300 samples, 1 epoch)

Model Gemma-4	CPU (Space)	GPU (local)	Peak RAM
E2B 4-bit	~5h	~15 min	~8 GB
E4B 4-bit	~8h	~30 min	~13 GB

Note: Heretic abliteration with winsorization q=0.95 (default mlabonne dataset) was too slow on CPU, full run ~25h (20 trials).

Features

• Model-agnostic: works with any HF causal LM
• Auto GPU/CPU detection with per-device optimized configs
• 4-bit QLoRA on CPU via bitsandbytes
• Auto-detect dataset format (messages, ShareGPT, Alpaca, text)
• Dynamic model dropdown from unsloth org (filtered, <12B params)
• Chat with streaming, thinking mode toggle, system prompt
• Browser disconnect auto-stops training
• Configurable training time limit via MAX_HOUR_TRAINING_TIME env var
• RAM estimation from HF API before model download
• Per-session logs (private, not shared between users)
• chunked_nll loss for lower peak memory