---
license: apache-2.0
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: text2text-generation
library_name: unsloth
---

# ModelCard for UnGPT-v1

## Model Details
- **Name:** UnGPT-v1
- **Foundation Model:** Mistral v0.3 (7B parameters)
- **Recommended Context Length:** 16k tokens
- **Fine-tuning Methodology:** LoRA-based training with Odds Ratio Preference Optimization method, using a combination of ebooks and synthetic data.

## Usage Instructions
Use the Alpaca format for prompts:
```
### Instruction:
{instruction}

### Input:
{input}

### Response:
```
**Example prompts**

For instructions, it is not recommended to deviate from the provided examples. For the input, a minimum is 10 sentences, but more can work as the model can handle longer context sizes (Thanks to the Mistral 7B v0.3 base model).

  1. **Completion Prompt:**
      ```
      ### Instruction:
      Continue writing the story while retaining writing style. Write about 10 sentences.
    
      ### Input:
      It was a dark and stormy night...
    
      ### Response:
      ```

 2. **Fill-in-the-middle Prompt:**
     ```
     ### Instruction:
     Fill in the missing part of the story ({{FILL_ME}}) with about 10 sentences while retaining the writing style.
    
     ### Input:
     The bus was speeding down the road, cops chasing after it. 
     {{FILL_ME}}
     She woke up to find herself in an unfamiliar room...
    
      ### Response:
     ```

## Dataset Preparation

For dataset acquisition and cleanup please refer steps 1 and 2 of my text-completion example, [molbal/llm-text-completion-finetune](https://github.com/molbal/llm-text-completion-finetune/).

Chunking: Split texts into chunks based on sentence boundaries, aiming for 100 sentences per example.
   - For completion examples, 90 sentences were used as input, 10 sentences as response.
   - For fill-in-the-middle examples, 80 + 10 sentences as input (before and after the {{FILL_ME}} placeholder, respectively), and 10 sentences as response.

The beauty of the ORPO method is that for a single prompt we can set both a positive and a negative example. I wanted the model to avoid 'GPTisms' so I had gpt4o-mini generate answers both for completion and FOM tasks and added them as a neative example.

The dataset used is ~15k examples, each approximately 9000 characters long including input, accepted and refused response. (Note these are characters not tokens)

## Training setup

- Fine-tuned the Mistral v0.3 foundation model using Unsloth and ORPO trainer.

- Training configuration:
  - Batch size: 1
  - Gradient accumulation steps: 4
  - Learning rate scheduler type: Linear
  - Optimizer: AdamW (8-bit)
  - Number of training epochs: 1

- Hardware
  - I used GPU accelerated containers from the provider vast.ai (My referral link: https://cloud.vast.ai/?ref_id=123492 ) and executed training for ~8 hours on a single RTX 4090. 

- Training costs
  - ~5€ for renting a GPU pod (+15€ in unsuccessful attempts)
  - ~5€ in OpenAI API costs for generating refusals

**Licensing and Citation**

- **License:** This model is licensed under the Apache License 2.0.
- **Citation:**
```bibtex
@misc{ungpt-v1,
  author = Bálint Molnár-Kaló,
  title = {UnGPT-v1: A Fine-tuned Mistral Model for Story Continuation},
  howpublished = {\url{https://huggingface.co/models/molbal/UnGPT-v1}},
  year = 2024
}
```