Llama-3.2-3B-Instruct-Reasoning/README.md

library_name, language, base_model

library_name	language	base_model
transformers	en	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.

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Base Model

• meta-llama/Llama-3-3b-instruct

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Model Details

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

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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

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

pip install transformers accelerate torch