| --- |
| library_name: transformers |
| tags: |
| - math |
| - lora |
| - science |
| - chemistry |
| - biology |
| - code |
| - text-generation-inference |
| - unsloth |
| - llama |
| license: apache-2.0 |
| datasets: |
| - HuggingFaceTB/smoltalk |
| language: |
| - en |
| - de |
| - es |
| - fr |
| - it |
| - pt |
| - hi |
| - th |
| base_model: |
| - meta-llama/Llama-3.2-1B-Instruct |
| --- |
| |
|  |
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| You can use ChatML & Alpaca format. |
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| You can chat with the model via this [space](https://huggingface.co/spaces/suayptalha/Chat-with-FastLlama). |
|
|
| **Overview:** |
|
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| 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. |
|
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| **Features:** |
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| Lightweight and Fast: Optimized to deliver Llama-class capabilities with reduced computational overhead. |
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| Fine-Tuned for Math Reasoning: Utilizes MetaMathQA-50k for better handling of complex mathematical problems and logical reasoning tasks. |
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| Instruction-Tuned: Pre-trained on instruction-following tasks, making it robust in understanding and executing detailed queries. |
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| Versatile Use Cases: Suitable for educational tools, tutoring systems, or any application requiring mathematical reasoning. |
|
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| **Performance Highlights:** |
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| Smaller Footprint: The model delivers comparable results to larger counterparts while operating efficiently on smaller hardware. |
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| Enhanced Accuracy: Demonstrates improved performance on mathematical QA benchmarks. |
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| Instruction Adherence: Retains high fidelity in understanding and following user instructions, even for complex queries. |
|
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| **Loading the Model:** |
| ```py |
| import torch |
| from 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:** |
|
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| Dataset: MetaMathQA-50k |
|
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| 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: |
|
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| Algebraic problems |
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| Geometric reasoning tasks |
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| Statistical and probabilistic questions |
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| Logical deduction problems |
|
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| **Model Fine-Tuning:** |
|
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| Fine-tuning was conducted using the following configuration: |
|
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| Learning Rate: 2e-4 |
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| Epochs: 1 |
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| Optimizer: AdamW |
|
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| Framework: Unsloth |
|
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| **License:** |
|
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| This model is licensed under the Apache 2.0 License. See the LICENSE file for details. |
|
|
| <a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> |
