---
library_name: transformers
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---

# LLaMA 3B Instruct Reasoning Model

This model is a **fine-tuned version of LLaMA 3B Instruct**, optimized for reasoning tasks such as step-by-step problem solving and logical question answering.

The model was fine-tuned using **LoRA (PEFT)** and later merged into the base model to create a **fully standalone model**.

---

## Base Model

- `meta-llama/Llama-3-3b-instruct`

---

## Model Details

- **Architecture:** LLaMA 3B
- **Fine-tuning method:** LoRA (merged)
- **Task:** Causal Language Modeling
- **Use case:** Reasoning / instruction-following

---

## Features

- Improved step-by-step reasoning 
- Better structured answers 
- Enhanced instruction following
- Suitable for logical tasks

---
## Training Details

This model was fine-tuned on a reasoning dataset from Hugging Face using LoRA.

The LoRA weights were merged with the base model to produce a standalone model for easier deployment and usage.

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shaw2037/Llama-3.2-3B-Instruct-Reasoning"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

prompt = "Solve step by step: If 2x + 3 = 11, what is x?"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```



## Limitations
May produce incorrect reasoning steps.

Can hallucinate in complex scenarios.

Not guaranteed to be mathematically perfect.


## Intended Use

## Suitable for:

reasoning experiments

educational projects

LLM research


## Not suitable for:

medical advice

legal advice

financial decisions

safety-critical applications


### Install dependencies

```bash
pip install transformers accelerate torch