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--- |
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library_name: transformers |
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tags: |
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- math |
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- lora |
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- science |
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- chemistry |
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- biology |
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- code |
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- text-generation-inference |
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- unsloth |
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- llama |
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license: apache-2.0 |
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datasets: |
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- HuggingFaceTB/smoltalk |
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language: |
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- en |
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- de |
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- es |
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- fr |
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- it |
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- pt |
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- hi |
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- th |
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base_model: |
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- meta-llama/Llama-3.2-1B-Instruct |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/FastLlama-3.2-1B-Instruct-GGUF |
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This is quantized version of [suayptalha/FastLlama-3.2-1B-Instruct](https://huggingface.co/suayptalha/FastLlama-3.2-1B-Instruct) created using llama.cpp |
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# Original Model Card |
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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). |
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**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:** |
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```py |
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import torch |
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from transformers import pipeline |
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model_id = "suayptalha/FastLlama-3.2-1B-Instruct" |
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pipe = pipeline( |
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"text-generation", |
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model=model_id, |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "system", "content": "You are a friendly assistant named FastLlama."}, |
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{"role": "user", "content": "Who are you?"}, |
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] |
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outputs = pipe( |
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messages, |
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max_new_tokens=256, |
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) |
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print(outputs[0]["generated_text"][-1]) |
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``` |
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**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. |
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[☕ Buy Me a Coffee](https://www.buymeacoffee.com/suayptalha) |
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