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suayptalha
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Model Details
Model Provider
suayptalha
Model Tree
meta-llama/Llama-3.2-1B-Instruct
Input Modalities
Output Modalities
Supported Functionality
You can use ChatML & Alpaca format.
You can chat with the model via this space.
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
import torchfrom transformers import pipeline model_id = "suayptalha/FastLlama-3.2-1B-Instruct"pipe = pipeline( "text-generation", model=model_id, device_map="auto",)messages = [ {"role": "system", "content": "You are a friendly assistant named FastLlama."}, {"role": "user", "content": "Who are you?"},]outputs = pipe( messages, max_new_tokens=256,)print(outputs[0]["generated_text"][-1])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.