justsomerandomdude264
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Math_Homework_Solver_Llama318B

Text Generation

text-generation-inference

4-bit precision

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justsomerandomdude264 commited on Aug 19, 2024

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@@ -24,7 +24,7 @@ This is a Large Language Model (LLM) fine-tuned to solve math problems with deta
 - **Fine-tuning Method**: PEFT (Parameter-Efficient Fine-Tuning) with QLoRA
 - **Quantization**: 4-bit quantization for reduced memory usage
 - **Training Framework**: Unsloth, optimized for efficient fine-tuning of large language models
-- **Training Environment**: Google Colab (free tier), NVIDIA T4 GPU (12GB VRAM), 12GB RAM
 - **Dataset Used**: TIGER-Lab/MathInstruct (Yue, X., Qu, X., Zhang, G., Fu, Y., Huang, W., Sun, H., Su, Y., & Chen, W. (2023). MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning. *arXiv preprint arXiv:2309.05653*.
 ), 560 selected math problems and solutions

 - **Fine-tuning Method**: PEFT (Parameter-Efficient Fine-Tuning) with QLoRA
 - **Quantization**: 4-bit quantization for reduced memory usage
 - **Training Framework**: Unsloth, optimized for efficient fine-tuning of large language models
+- **Training Environment**: Google Colab (free tier), NVIDIA T4 GPU (16GB VRAM), 12GB RAM
 - **Dataset Used**: TIGER-Lab/MathInstruct (Yue, X., Qu, X., Zhang, G., Fu, Y., Huang, W., Sun, H., Su, Y., & Chen, W. (2023). MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning. *arXiv preprint arXiv:2309.05653*.
 ), 560 selected math problems and solutions