huggingface-llm-trainer

Files (5)

• CHANGELOG.md
• CURRENT.md
• README.md
• SKILL.md
• metadata.json

SKILL.md (first lines)

# TRL Training on Hugging Face Jobs

## Overview

Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub.

**TRL provides multiple training methods:**
- **SFT** (Supervised Fine-Tuning) - Standard instruction tuning
- **DPO** (Direct Preference Optimization) - Alignment from preference data
- **GRPO** (Group Relative Policy Optimization) - Online RL training
- **Reward Modeling** - Train reward models for RLHF

**For detailed TRL method documentation:**
```python
hf_doc_search("your query", product="trl")
hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer")  # SFT
hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer")  # DPO
# etc.
```

**See also:** `references/training_methods.md` for method overviews and selection guidance

## When to Use This Skill

Use this skill when users want to:
- Fine-tune language models on cloud GPUs without local infrastructure
- Train with TRL methods (SFT, DPO, GRPO, etc.)
- Run training jobs on Hugging Face Jobs infrastructure
- Convert trained models to GGUF for local deployment (Ollama, LM Studio, llama.cpp)
- Ensure trained models are permanently saved to the Hub
- Use modern workflows with optimized defaults

### When to Use Unsloth

Use **Unsloth** (`references/unsloth.md`) instead of standard TRL when:
- **Limited GPU memory** - Unsloth uses ~60% less VRAM
- **Speed matters** - Unsloth is ~2x faster
- Training **large models (>13B)** - memory efficiency is critical
- Training **Vision-Language Models (VLMs)** - Unsloth has `FastVisionModel` support

See `references/unsloth.md` for complete Unsloth documentation and `scripts/unsloth_sft_example.py` for a production-ready training script.

## Key Directives

When assisting with training jobs:

1. **ALWAYS use `hf_jobs()` MCP tool** - Submit jobs using `hf_jobs("uv", {...})`, NOT bash `trl-jobs` commands. The `script` parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to `hf_jobs()`. If user asks to "train a model", "fine-tune", or similar requests, you MUST create the training script AND submit the job immediately using `hf_jobs()`.

2. **Always include Trackio** - Every training script should include Trackio for real-time monitoring. Use example scripts in `scripts/` as templates.

3. **Provide job details after submission** - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later.

4. **Use example scripts as templates** - Reference `scripts/train_sft_example.py`, `scripts/train_dpo_example.py`, etc. as starting points.

## Local Script Execution

Repository scripts use PEP 723 inline dependencies. Run them with `uv run`:
```bash
uv run scripts/estimate_cost.py --help
uv run scripts/dataset_inspector.py --help
```

## Prerequisites Checklist

Before starting any training job, verify:

### ✅ **Account & Authentication**
- Hugging Face Account with [Pro](https://hf.co/pro), [Team](https://hf.co/enterprise), or [Enterprise](https://hf.co/enterprise) plan (Jobs require paid plan)
- Authenticated login: Check with `hf_whoami()`
- **HF_TOKEN for Hub Push** ⚠️ CRITICAL - Training environment is ephemeral, must push to Hub or ALL training results are lost
- Token must have write permissions  
- **MUST pass `secrets={"HF_TOKEN": "$HF_TOKEN"}` in job config** to make token available (the `$HF_TOKEN` syntax
  references your actual token value)

### ✅ **Dataset Requirements**
- Dataset must exist on Hub or be loadable via `datasets.load_dataset()`
- Format must match training method (SFT: "messages"/text/prompt-completion; DPO: chosen/rejected; GRPO: prompt-only)
- **ALWAYS validate unknown datasets** before GPU training to prevent format failures (see Dataset Validation section below)
- Size appropriate for hardware (Demo: 50-100 examples on t4-small; Production: 1K-10K+ on a10g-large/a100-large)

### ⚠️ **Critical Settings**
- **Timeout must exceed expected training time** - Default 30min is TOO SHORT for most training. Minimum recommended: 1-2 hours. Job fails and loses all progress if timeout is exceeded.
- **Hub push must be enabled** - Config: `push_to_hub=True`, `hub_model_id="username/model-name"`; Job: `secrets={"HF_TOKEN": "$HF_TOKEN"}`

## Asynchronous Job Guidelines

**⚠️ IMPORTANT: Training jobs run asynchronously and can take hours**

### Action Required

**When user requests training:**
1. **Create the training script** with Trackio included (use `scripts/train_sft_example.py` as template)
2. **Submit immediately** using `hf_jobs()` MCP tool with script content inline - don't save to file unless user requests
3. **Report submission** with job ID, monitoring URL, and estimated time
4. **Wait for user** to request status checks - don't poll automatically

### Ground Rules
- **Jobs run in background** - Submission returns immediately; training continues independently
- **Initial logs delayed** - Can take 30-60 seconds for logs to appear
- **User checks status** - Wait for user to request status updates
- **Avoid polling** - Check logs only on user request; provide monitoring links instead

### After Submission

**Provide to user:**
- ✅ Job ID and monitoring URL
- ✅ Expected completion time
- ✅ Trackio dashboard URL
- ✅ Note that user can request status checks later

**Example Response:**
```
✅ Job submitted successfully!

Job ID: abc123xyz
Monitor: https://huggingface.co/jobs/username/abc123xyz

Expected time: ~2 hours
Estimated cost: ~$10

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