The activeDap/Llama-3.2-3B_hh_helpful model is a 3.2 billion parameter language model fine-tuned from Meta's Llama-3.2-3B architecture. It was specifically trained on the activeDap/sft-hh-data dataset using Supervised Fine-Tuning (SFT) with a focus on prompt-completion tasks and assistant-only loss. This model is optimized for generating helpful and coherent responses in conversational or instruction-following scenarios, leveraging its 32768 token context length.
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activeDap/Llama-3.2-3B_hh_helpful Overview
This model is a specialized fine-tune of the Meta Llama-3.2-3B base model, developed by activeDap. It has been trained using Supervised Fine-Tuning (SFT) on the activeDap/sft-hh-data dataset, which focuses on helpfulness and human feedback-style data. The training process utilized the Transformers and TRL libraries, configuring for prompt-completion tasks with assistant-only loss.
Key Capabilities
- Instruction Following: Enhanced ability to follow instructions and generate relevant responses due to its fine-tuning on a helpfulness-oriented dataset.
- Conversational AI: Optimized for generating coherent and contextually appropriate replies in dialogue systems.
- Efficient Inference: As a 3.2 billion parameter model, it offers a balance between performance and computational efficiency, suitable for various deployment scenarios.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing and generating longer sequences.
Good For
- Chatbots and Virtual Assistants: Ideal for applications requiring helpful and informative conversational agents.
- Content Generation: Generating text based on specific prompts, particularly in question-answering or explanatory contexts.
- Research and Development: A strong base for further fine-tuning on domain-specific helpfulness datasets.