Neelectric/Llama-3.1-8B-Instruct_SFT_sciencev00.21
Neelectric/Llama-3.1-8B-Instruct_SFT_sciencev00.21 is an 8 billion parameter instruction-tuned language model, fine-tuned by Neelectric from Meta's Llama-3.1-8B-Instruct base model. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, leveraging its instruction-following capabilities derived from the Llama-3.1 architecture. The model has a context length of 32768 tokens.
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Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_sciencev00.21 is an 8 billion parameter instruction-tuned language model, developed by Neelectric. It is a fine-tuned variant of the meta-llama/Llama-3.1-8B-Instruct base model, leveraging its robust architecture and instruction-following capabilities. The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, indicating a focus on enhancing its ability to follow specific instructions and generate coherent responses.
Key Capabilities
- Instruction Following: Inherits and refines the instruction-following capabilities of the Llama-3.1-8B-Instruct base model through SFT.
- Text Generation: Capable of generating human-like text based on given prompts and instructions.
- Context Handling: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL library. The training process utilized specific versions of key frameworks, including TRL 0.28.0.dev0, Transformers 4.57.6, Pytorch 2.9.0, Datasets 4.5.0, and Tokenizers 0.22.2. This fine-tuning aims to adapt the base model for improved performance in instruction-based tasks.
Good For
- General-purpose instruction-tuned text generation.
- Applications requiring a model with a large context window for processing extensive inputs or generating detailed outputs.
- Developers looking for a fine-tuned Llama-3.1 variant with enhanced instruction-following.