ChuGyouk/F_R11_T3
ChuGyouk/F_R11_T3 is an 8 billion parameter language model, fine-tuned from ChuGyouk/F_R11 using the TRL library. This model is designed for general text generation tasks, leveraging its 32768 token context length to handle extensive inputs. Its training via Supervised Fine-Tuning (SFT) aims to enhance its conversational and response generation capabilities.
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Model Overview
ChuGyouk/F_R11_T3 is an 8 billion parameter language model, representing a fine-tuned iteration of the ChuGyouk/F_R11 base model. It was developed by ChuGyouk and trained using the Transformer Reinforcement Learning (TRL) library, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Characteristics
- Base Model: Fine-tuned from ChuGyouk/F_R11.
- Training Framework: Utilizes the TRL library for efficient fine-tuning.
- Training Method: Employs Supervised Fine-Tuning (SFT).
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to process and generate longer, more coherent texts.
Intended Use Cases
This model is suitable for a variety of text generation tasks, particularly those benefiting from its fine-tuned nature and extended context window. Developers can integrate it using the transformers library for applications such as:
- Question Answering: Generating detailed responses to user queries.
- Conversational AI: Participating in extended dialogues.
- Content Creation: Producing longer-form text based on prompts.
Technical Details
The training procedure involved specific versions of key frameworks:
- TRL: 0.24.0
- Transformers: 5.2.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Further details on the training process can be visualized via Weights & Biases.