---
base_model: google/gemma-2-9b-it
library_name: transformers
model_name: Velma-9b
tags:
- generated_from_trainer
- trl
- sft
- reasoning
licence: license
datasets:
- openai/gsm8k
pipeline_tag: text-generation
---

# Velma-9b

## Model Overview

**Velma-9b** is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it), optimized to improve reasoning capabilities. The model has been trained using the [GSM8K dataset](https://huggingface.co/datasets/openai/gsm8k), a benchmark dataset designed to enhance mathematical and logical reasoning skills in language models.

This fine-tuning process allows Velma-9b to excel at structured problem-solving, step-by-step reasoning, and logical inference, making it an ideal choice for tasks requiring in-depth analytical thinking.

## Features

- **Fine-Tuned on GSM8K:** Enhanced for mathematical reasoning and step-by-step logical problem-solving.
- **Transformer-Based:** Built on the powerful `gemma-2-9b-it` architecture.
- **Optimized for SFT (Supervised Fine-Tuning):** Fine-tuned using [TRL (Transformer Reinforcement Learning)](https://github.com/huggingface/trl) for improved inference and structured output generation.
- **Efficient Deployment:** Compatible with `transformers` and supports GPU acceleration for fast inference.

## Quick Start

Use the following code to generate text using Velma-9b:

```python
from transformers import pipeline

# Initialize the pipeline
generator = pipeline("text-generation", model="AmirMohseni/Velma-9b", device="cuda")

# Example prompt
question = "If you had a time machine but could only go to the past or the future once and never return, which would you choose and why?"

# Generate output
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training Procedure

Velma-9b was fine-tuned using the **Supervised Fine-Tuning (SFT)** approach with the GSM8K dataset. This dataset contains high-quality mathematical and logical reasoning problems that help models develop structured thinking and problem-solving skills.

### Training Details

- **Base Model:** [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
- **Dataset Used:** [GSM8K](https://huggingface.co/datasets/openai/gsm8k)
- **Fine-Tuning Method:** SFT(STaR) using [TRL](https://github.com/huggingface/trl)
- **Optimization Objective:** Supervised fine-tuning to enhance structured reasoning

### Framework Versions

- **TRL:** `0.12.1`
- **Transformers:** `4.46.3`
- **PyTorch:** `2.1.1`
- **Datasets:** `3.1.0`
- **Tokenizers:** `0.20.3`

## Use Cases

Velma-9b is best suited for tasks requiring structured reasoning and logical inference:

- **Mathematical & Logical Reasoning Tasks:** Providing step-by-step explanations and structured problem-solving.
- **AI Research & Experimentation:** Evaluating fine-tuning strategies for reasoning-focused language models.