j05hr3d/Llama-3.2-3B-Instruct-C_M_T-AUX_CT2_CE_EE
j05hr3d/Llama-3.2-3B-Instruct-C_M_T-AUX_CT2_CE_EE is a 3.2 billion parameter instruction-tuned causal language model, fine-tuned by j05hr3d from the meta-llama/Llama-3.2-3B-Instruct base model. This model was trained using SFT with the TRL framework, offering a 32768 token context length. It is designed for general instruction-following tasks, leveraging its fine-tuned capabilities for conversational AI and text generation.
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Overview
This model, j05hr3d/Llama-3.2-3B-Instruct-C_M_T-AUX_CT2_CE_EE, is an instruction-tuned variant of the meta-llama/Llama-3.2-3B-Instruct base model. It features 3.2 billion parameters and supports a substantial context length of 32768 tokens, making it suitable for processing longer prompts and generating extended responses. The fine-tuning process utilized Supervised Fine-Tuning (SFT) with the TRL library, enhancing its ability to follow instructions and engage in conversational tasks.
Key Capabilities
- Instruction Following: Optimized through SFT to accurately interpret and respond to user instructions.
- Text Generation: Capable of generating coherent and contextually relevant text based on prompts.
- Extended Context: Benefits from a 32768-token context window, allowing for more complex and detailed interactions.
Training Details
The model was fine-tuned using the TRL framework (version 0.27.1) with Transformers (version 4.57.6) and PyTorch (version 2.10.0+cu128). The training procedure focused on SFT to improve its performance on instruction-based tasks. Further details on the training run can be explored via the associated Weights & Biases project.
Good For
- Conversational AI: Developing chatbots or virtual assistants that require strong instruction adherence.
- General Text Generation: Applications needing to generate creative or informative text based on specific prompts.
- Prototyping: A good choice for developers looking for a capable 3B-parameter model with a large context window for various NLP tasks.