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---
library_name: transformers
tags:
- math
- lora
- science
- chemistry
- biology
- code
- text-generation-inference
- unsloth
license: apache-2.0
datasets:
- HuggingFaceTB/smoltalk
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
---
You can use ChatML & Alpaca format.
**Overview**
FastLlama is a highly optimized version of the Llama-3.2-1B-Instruct model. Designed for superior performance in constrained environments, it combines speed, compactness, and high accuracy. This version has been fine-tuned using the MetaMathQA-50k section of the HuggingFaceTB/smoltalk dataset to enhance its mathematical reasoning and problem-solving abilities.
**Features**
Lightweight and Fast: Optimized to deliver Llama-class capabilities with reduced computational overhead.
Fine-Tuned for Math Reasoning: Utilizes MetaMathQA-50k for better handling of complex mathematical problems and logical reasoning tasks.
Instruction-Tuned: Pre-trained on instruction-following tasks, making it robust in understanding and executing detailed queries.
Versatile Use Cases: Suitable for educational tools, tutoring systems, or any application requiring mathematical reasoning.
**Performance Highlights**
Smaller Footprint: The model delivers comparable results to larger counterparts while operating efficiently on smaller hardware.
Enhanced Accuracy: Demonstrates improved performance on mathematical QA benchmarks.
Instruction Adherence: Retains high fidelity in understanding and following user instructions, even for complex queries.
**Loading the Model**
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
model_name = "your-hf-username/fastllama-3.2-1b-instruct-metamathqa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
input_text = "Solve for x: 2x + 3 = 7"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
**Dataset**
Dataset: MetaMathQA-50k
The MetaMathQA-50k subset of HuggingFaceTB/smoltalk was selected for fine-tuning due to its focus on mathematical reasoning, multi-step problem-solving, and logical inference. The dataset includes:
Algebraic problems
Geometric reasoning tasks
Statistical and probabilistic questions
Logical deduction problems
**Model Fine-Tuning**
Fine-tuning was conducted using the following configuration:
Learning Rate: 2e-4
Epochs: 1
Optimizer: AdamW
Framework: Unsloth
**License**
This model is licensed under the Apache 2.0 License. See the LICENSE file for details. |