Neelectric/Llama-3.1-8B-Instruct_SFT_sciencesp_ewc_v00.01
Neelectric/Llama-3.1-8B-Instruct_SFT_sciencesp_ewc_v00.01 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 has been specifically trained using Supervised Fine-Tuning (SFT) with TRL, building upon its base model's 32768 token context length. It is designed for general instruction-following tasks, leveraging its fine-tuned capabilities for diverse applications.
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Model Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_sciencesp_ewc_v00.01 is an 8 billion parameter instruction-tuned language model, fine-tuned by Neelectric from the base meta-llama/Llama-3.1-8B-Instruct model. This model leverages a 32768 token context length, making it suitable for processing longer inputs and generating comprehensive responses.
Training Details
This model was trained using Supervised Fine-Tuning (SFT) via the TRL library. The training process utilized specific framework versions:
- TRL: 1.1.0.dev0
- Transformers: 4.57.6
- Pytorch: 2.9.0
- Datasets: 5.0.0
- Tokenizers: 0.22.2
Key Capabilities
- Instruction Following: Excels at understanding and executing user instructions due to its SFT training.
- Text Generation: Capable of generating coherent and contextually relevant text based on prompts.
- General Purpose: Suitable for a wide range of natural language processing tasks, inheriting the robust capabilities of the Llama-3.1-8B-Instruct base model.
Usage
Developers can easily integrate this model using the Hugging Face transformers library, as demonstrated in the quick start example for text generation tasks.