---
title: unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit (Research Training)
emoji: 🧪
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.17.0
app_file: app.py
pinned: false
license: mit
---

# Model Fine-Tuning Project

## Overview

- **Goal**: Fine-tune unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit using pre-tokenized JSONL dataset
- **Model**: `unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit`
  - **Important**: Already 4-bit quantized - do not quantize further
- **Dataset**: `phi4-cognitive-dataset`

⚠️ **RESEARCH TRAINING PHASE ONLY**: This space is being used for training purposes and does not provide interactive model outputs.

### Dataset Specs
- Entries under 2048 tokens
- Fields: `prompt_number`, `article_id`, `conversations`
- Process in ascending `prompt_number` order
- Pre-tokenized dataset - no additional tokenization needed
    
### Hardware
- GPU: 1x L40S (48GB VRAM)
- RAM: 62GB
- CPU: 8 cores

## Environment Variables (.env)

- `HF_TOKEN`: Hugging Face API token 
- `HF_USERNAME`: Hugging Face username 
- `HF_SPACE_NAME`: Target space name 

## Files

### 1. `app.py`
- Training status dashboard
- No interactive model demo (research phase only)

### 2. `transformers_config.json`
- Configuration for Hugging Face Transformers
- Contains: model parameters, hardware settings, optimizer details
- Specifies pre-tokenized dataset handling

### 3. `run_cloud_training.py`
- Loads pre-tokenized dataset, sorts by `prompt_number`, initiates training
1. Load and sort JSONL by `prompt_number` 
2. Use pre-tokenized input_ids directly (no tokenization)
3. Initialize with parameters from config
4. Execute training with metrics, checkpoints, error handling
- Uses Hugging Face's Trainer API with custom pre-tokenized data collator

### 4. `requirements.txt`
- Python dependencies: `transformers`, `datasets`, `torch`, etc.
- Contains unsloth for optimized training

### 5. `upload_to_space.py`
- Update model and space directly using HF API

## Implementation Notes

### Best Practices
- Dataset is pre-tokenized and sorted by `prompt_number`
- Settings stored in config file, avoiding hardcoding
- Hardware-optimized training parameters
- Gradient checkpointing and mixed precision training
- Complete logging for monitoring progress

### Model Repository

This space hosts a fine-tuned version of the [unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit) model.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference