ai-nexuz/llama-3.2-1b-instruct-fine-tuned

LLaMA-3.2-1B-Instruct Fine-Tuned Model

Model Card for Hugging Face Repository

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Model Summary

This is a fine-tuned version of the LLaMA-3.2-1B-Instruct model. Fine-tuned using the kanhatakeyama/wizardlm8x22b-logical-math-coding-sft dataset, this model is specialized in logical reasoning, mathematical problem-solving, and coding tasks. Training was performed using Unsloth on Google Colab, optimized for performance and usability.

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Model Details

• Model Name: LLaMA-3.2-1B-Instruct (Fine-tuned)
• Base Model: LLaMA-3.2-1B-Instruct
• Fine-Tuning Dataset: kanhatakeyama/wizardlm8x22b-logical-math-coding-sft
• Fine-Tuning Framework: Unsloth
• Parameters: 1 Billion
• Domain: Logical Reasoning, Mathematics, Coding
• Tags: llama, fine-tuning, instruction-following, math, coding, logical-reasoning, unsloth

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Fine-Tuning Dataset

The fine-tuning dataset, kanhatakeyama/wizardlm8x22b-logical-math-coding-sft, is curated for advanced reasoning tasks. It contains:

• Logical reasoning scenarios
• Step-by-step mathematical solutions
• Complex code generation and debugging examples

Dataset Link: kanhatakeyama/wizardlm8x22b-logical-math-coding-sft

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Intended Use

This model is ideal for tasks such as:

1. Logical Problem Solving: Derive conclusions and explanations for logical questions.
2. Mathematics: Solve algebra, calculus, and other mathematical problems.
3. Coding: Generate, debug, and explain programming code in various languages.
4. Instruction-Following: Handle user queries with clear and concise answers.

Example Applications:

• AI tutors
• Logical reasoning assistants
• Math-solving bots
• Code generation and debugging tools

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Usage

Installation

To use this model, install the required dependencies:

pip install transformers datasets torch accelerate

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_name = "ai-nexuz/llama-3.2-1b-instruct-fine-tuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Generating Outputs

prompt = "Solve this equation: 2x + 3 = 7. Find x."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

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Model Training

Hardware

• Platform: Google Colab Pro
• GPU: NVIDIA Tesla T4

Training Configuration

• Batch Size: 32
• Epochs: 1

Frameworks Used

• Unsloth: For efficient training
• Hugging Face Transformers: For model and tokenizer handling

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Limitations

While this model is highly proficient in logical reasoning, mathematics, and coding tasks, there are some limitations:

• May produce inaccurate results for ambiguous or poorly-defined prompts.
• Performance may degrade for highly specialized or niche coding languages.

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Deployment

Using Gradio for Web UI

pip install gradio

import gradio as gr

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=200)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

gr.Interface(fn=generate_response, inputs="text", outputs="text").launch()

Hugging Face Inference API

This model can also be accessed using the Hugging Face Inference API for hosted deployment:

from transformers import pipeline

pipe = pipeline("text-generation", model="ai-nexuz/llama-3.2-1b-instruct-fine-tuned")
result = pipe("Explain the concept of recursion in programming.")
print(result)

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Acknowledgements

This fine-tuning work was made possible by:

• Hugging Face for their exceptional library and dataset hosting.
• Unsloth for providing an efficient fine-tuning framework.
• Google Colab for GPU resources.

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Citation

If you use this model in your research or project, please cite it as:

@model{llama31b_instruct_finetuned,
  title={Fine-Tuned LLaMA-3.2-1B-Instruct},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/your-huggingface-repo/llama-3.2-1b-instruct-finetuned},
}

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Licensing

This model is released under the Apache 2.0 License. See LICENSE for details.

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Tags:
llama fine-tuning math coding logical-reasoning instruction-following transformers

Summary:
A fine-tuned version of LLaMA-3.2-1B-Instruct specializing in logical reasoning, math problem-solving, and code generation. Perfect for AI-driven tutoring, programming assistance, and logical problem-solving tasks.