Neelectric/Llama-3.1-8B-Instruct_SFT_mathfisher_v00.02_s43
Neelectric/Llama-3.1-8B-Instruct_SFT_mathfisher_v00.02_s43 is an 8 billion parameter instruction-tuned model, fine-tuned from Meta's Llama-3.1-8B-Instruct. It was trained by Neelectric using the TRL framework on the OpenR1-Math-220k_all_Llama3_4096toks dataset. This model is specifically optimized for mathematical reasoning and problem-solving tasks, leveraging its 32768 token context length.
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Overview
This model, Neelectric/Llama-3.1-8B-Instruct_SFT_mathfisher_v00.02_s43, is an 8 billion parameter language model derived from Meta's Llama-3.1-8B-Instruct. It has been specifically fine-tuned by Neelectric using the TRL framework.
Key Capabilities
- Mathematical Reasoning: The model's primary strength lies in its fine-tuning on the
Neelectric/OpenR1-Math-220k_all_Llama3_4096toksdataset, indicating a specialization in mathematical problem-solving and related tasks. - Instruction Following: As an instruction-tuned model, it is designed to follow user prompts and generate relevant responses effectively.
- Context Handling: With a context length of 32768 tokens, it can process and understand longer inputs, which is beneficial for complex multi-step problems or detailed instructions.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL library. The training utilized specific versions of frameworks including TRL 1.1.0.dev0, Transformers 4.57.6, Pytorch 2.9.0, Datasets 4.8.5, and Tokenizers 0.22.2.
Should I use this for my use case?
This model is particularly well-suited for applications requiring strong mathematical reasoning, problem-solving, or detailed instruction following within a mathematical context. If your use case involves generating solutions to math problems, explaining mathematical concepts, or processing numerical data, this fine-tuned model offers a specialized advantage over general-purpose LLMs.