j05hr3d/Llama-3.2-3B-Instruct-C_M_T-ALPACA
j05hr3d/Llama-3.2-3B-Instruct-C_M_T-ALPACA is a 3.2 billion parameter instruction-tuned language model, fine-tuned by j05hr3d from the Meta Llama-3.2-3B-Instruct base model. This model was trained using Supervised Fine-Tuning (SFT) with the TRL library, making it suitable for general instruction-following tasks. It features a 32768-token context length, providing extensive capacity for processing longer prompts and generating detailed responses.
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Model Overview
This model, j05hr3d/Llama-3.2-3B-Instruct-C_M_T-ALPACA, is a 3.2 billion parameter instruction-tuned variant of the meta-llama/Llama-3.2-3B-Instruct base model. It has been specifically fine-tuned using Supervised Fine-Tuning (SFT) with the TRL library, which is designed for Transformer Reinforcement Learning.
Key Capabilities
- Instruction Following: Optimized to understand and execute user instructions effectively, making it suitable for a wide range of conversational and task-oriented applications.
- Extended Context: Benefits from a substantial 32768-token context window, allowing it to process and generate longer, more coherent texts while maintaining context over extended interactions.
- Base Model Heritage: Built upon the Llama-3.2-3B-Instruct architecture, inheriting its foundational language understanding and generation capabilities.
Training Details
The model's fine-tuning process utilized SFT, a common and effective method for adapting pre-trained language models to specific instruction-following behaviors. The training was conducted using TRL version 0.27.1, Transformers 4.57.6, Pytorch 2.10.0+cu128, Datasets 4.8.4, and Tokenizers 0.22.2.
Good For
- General-purpose chatbots: Its instruction-following capabilities make it a strong candidate for interactive agents.
- Content generation: Can be used for generating various forms of text based on specific prompts.
- Research and experimentation: Provides a fine-tuned Llama-3.2 variant for exploring SFT-based adaptations.