ChuGyouk/F_R17_T4 is an 8 billion parameter language model, fine-tuned from the ChuGyouk/F_R17 base model using SFT (Supervised Fine-Tuning) with TRL. This model is designed for general text generation tasks, leveraging a 32,768 token context length for processing longer inputs. It is suitable for applications requiring coherent and contextually relevant text outputs.
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Overview
ChuGyouk/F_R17_T4 is an 8 billion parameter language model, fine-tuned from the ChuGyouk/F_R17 base model. The fine-tuning process utilized SFT (Supervised Fine-Tuning) with the TRL (Transformer Reinforcement Learning) library, enhancing its capabilities for various text generation tasks. This model supports a substantial context length of 32,768 tokens, allowing it to handle more extensive inputs and generate longer, more coherent responses.
Key Capabilities
- General Text Generation: Capable of producing human-like text based on given prompts.
- Extended Context Handling: Benefits from a 32,768 token context window, enabling better understanding and generation for longer conversations or documents.
- Fine-tuned Performance: Leverages SFT for improved instruction following and response quality.
Training Details
The model was trained using the TRL library (version 0.24.0) within a PyTorch (2.10.0) and Transformers (5.2.0) framework. The training procedure involved Supervised Fine-Tuning, as indicated by the use of SFT. Developers can integrate this model using the Hugging Face pipeline for text generation tasks.
Good For
- Generating creative content or responses to open-ended questions.
- Applications requiring models to maintain context over longer interactions.
- Developers looking for a fine-tuned 8B parameter model for general language tasks.