Velma-9b

Model Overview

Velma-9b is a fine-tuned version of google/gemma-2-9b-it, optimized to improve reasoning capabilities. The model has been trained using the GSM8K dataset, 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) 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:

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
• Dataset Used: GSM8K
• Fine-Tuning Method: SFT(STaR) using 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.