Neelectric/Llama-3.1-8B-Instruct_SFT_sciencev00.06
Neelectric/Llama-3.1-8B-Instruct_SFT_sciencev00.06 is an 8 billion parameter instruction-tuned causal language model developed by Neelectric, fine-tuned from Meta's Llama-3.1-8B-Instruct. This model specializes in scientific reasoning, having been trained on a curated dataset focused on scientific content. With a 32768-token context length, it is optimized for tasks requiring in-depth scientific understanding and logical inference.
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Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_sciencev00.06 is an 8 billion parameter instruction-tuned model, building upon the robust Meta Llama-3.1-8B-Instruct architecture. It has been specifically fine-tuned by Neelectric using the Neelectric/Replay_0.02.MoT_science.wildguardmix_reasoning.Llama3_4096toks dataset, which emphasizes scientific reasoning.
Key Capabilities
- Scientific Reasoning: Enhanced performance on tasks requiring scientific understanding and logical inference due to specialized fine-tuning.
- Instruction Following: Inherits strong instruction-following capabilities from its Llama-3.1-8B-Instruct base.
- Extended Context: Supports a context length of 32768 tokens, beneficial for processing longer scientific texts or complex problem descriptions.
Training Details
The model was trained using the TRL (Transformers Reinforcement Learning) library, specifically employing a Supervised Fine-Tuning (SFT) approach. The training leveraged frameworks including TRL 0.28.0.dev0, Transformers 4.57.6, Pytorch 2.9.0, Datasets 4.5.0, and Tokenizers 0.22.2.
Use Cases
This model is particularly well-suited for applications in scientific domains, such as:
- Answering scientific questions.
- Assisting with scientific research and analysis.
- Generating explanations for scientific concepts.
- Tasks requiring logical reasoning within a scientific context.