HoangCuongNguyen/gemma-2-9b-safetysft
HoangCuongNguyen/gemma-2-9b-safetysft is a 9 billion parameter language model fine-tuned from an unspecified base model, utilizing the TRL framework for its training. This model is designed for text generation tasks, with a context length of 16384 tokens. Its fine-tuning process suggests an optimization for safety-aligned responses, making it suitable for applications requiring moderated or controlled text outputs.
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Model Overview
HoangCuongNguyen/gemma-2-9b-safetysft is a 9 billion parameter language model that has been fine-tuned using the TRL (Transformers Reinforcement Learning) framework. While the specific base model is not detailed, the fine-tuning process indicates a specialization for particular text generation tasks.
Key Capabilities
- Text Generation: The model is capable of generating human-like text based on given prompts, as demonstrated by the quick start example.
- Safety-Aligned Fine-tuning: The "safetysft" in its name suggests that the model has undergone fine-tuning for safety, aiming to produce moderated or controlled outputs.
- Context Length: It supports a context length of 16384 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model was trained using SFT (Supervised Fine-Tuning), a common method for adapting pre-trained language models to specific tasks or behaviors. The training leveraged TRL version 1.0.0, Transformers 5.5.3, Pytorch 2.11.0+cu128, Datasets 4.8.4, and Tokenizers 0.22.2.
When to Use This Model
This model is suitable for developers looking for a 9 billion parameter model for text generation, particularly in scenarios where safety or specific response moderation is a concern due to its safety-aligned fine-tuning.