README.md

---
language:
- fr
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- summarizer
- lora
base_model: unsloth/gemma-2b-it-bnb-4bit
---

# Uploaded as lora model

- **Developed by:** Labagaite
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit

# Training Logs

## Traning metrics
![Evaluation Loss Plot](eval_loss_plot.png)

## Evaluation score
## Évaluation des rapports générés par les deux modèles d'IA

### Modèle de base (unsloth/gemma-2b-it-bnb-4bit)
1. **Performance de la structuration du rapport**: 6/10
2. **Qualité du langage**: 7/10
3. **Cohérence**: 6/10

### Modèle fine-tuned (gemma-Summarizer-2b-it-bnb-4bit)
1. **Performance de la structuration du rapport**: 8/10
2. **Qualité du langage**: 8/10
3. **Cohérence**: 8/10

### Score global
- Modèle de base: 6.3/10
- Modèle fine-tuned: 8/10

### Conclusion
Le modèle fine-tuned a clairement surpassé le modèle de base en termes de structuration du rapport, qualité du langage et cohérence. Le rapport généré par le modèle fine-tuned est plus clair, plus fluide et mieux organisé. Il offre une analyse plus approfondie et une meilleure compréhension des sujets abordés. En revanche, le modèle de base présente quelques lacunes en termes de cohérence et de structuration. Il pourrait bénéficier d'une amélioration pour offrir des rapports plus percutants et informatifs.
[Evaluation report and scoring](evaluation/run-unsloth/gemma-2b-it-bnb-4bit-7449/Model_evaluator-gemma-Summarizer-2b-it-bnb-4bit.md)

## Wandb logs
You can view the training logs [<img src="https://raw.githubusercontent.com/wandb/wandb/main/docs/README_images/logo-light.svg" width="200"/>](https://wandb.ai/william-derue/LLM-summarizer_trainer/runs/nlrru9au).

## Training details

### training data
- Dataset : [fr-summarizer-dataset](https://huggingface.co/datasets/Labagaite/fr-summarizer-dataset)
- Data-size : 7.65 MB
- train : 1.97k rows
- validation : 440 rows
- roles : user , assistant
- Format chatml "role": "role", "content": "content", "user": "user", "assistant": "assistant"
<br>
*French audio podcast transcription*

# Project details
[<img src="https://avatars.githubusercontent.com/u/116890814?v=4" width="100"/>](https://github.com/WillIsback/Report_Maker)
Fine-tuned on French audio podcast transcription data for summarization task. As a result, the model is able to summarize French audio podcast transcription data.
The model will be used for an AI application: [Report Maker](https://github.com/WillIsback/Report_Maker) wich is a powerful tool designed to automate the process of transcribing and summarizing meetings.
It leverages state-of-the-art machine learning models to provide detailed and accurate reports.

This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
This gemma was trained with [LLM summarizer trainer](images/Llm_Summarizer_trainer_icon-removebg.png)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
**LLM summarizer trainer**
[<img src="https://github.com/WillIsback/LLM_Summarizer_Trainer/blob/main/images/Llm_Summarizer_trainer_icon-removebg.png?raw=true" width="150"/>](https://github.com/WillIsback/LLM_Summarizer_Trainer)