README.md

---
license: apache-2.0
datasets:
- kanhatakeyama/wizardlm8x22b-logical-math-coding-sft
base_model:
- unsloth/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- llm
- maths
- coding
- reasoning
- tech
- unsloth
- trl
- sft
---
 

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

**Model Card for Hugging Face Repository**

---

## 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.

---

## 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`

---

## 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](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft)

---

## 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

---

## Usage

### Installation

To use this model, install the required dependencies:
```bash
pip install transformers datasets torch accelerate
```

### Loading the Model

```python
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

```python
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)
```

---

 

## 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

---

## 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.

---

## Deployment

### Using Gradio for Web UI

```bash
pip install gradio
```

```python
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:
```python
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)
```

---

## 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.

---

## 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},
}
```

---

## Licensing

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

---

**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.