library_name, language, base_model
| library_name | language | base_model | ||
|---|---|---|---|---|
| transformers |
|
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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
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