ChuGyouk/F_R1_1_4b_T5 is a 4 billion parameter T5-based language model developed by ChuGyouk, fine-tuned from ChuGyouk/F_R1_1_4b. This model has been trained using Supervised Fine-Tuning (SFT) with the TRL framework, making it suitable for text generation tasks. It offers a context length of 32768 tokens, providing extensive capacity for processing and generating long sequences of text.
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Overview
ChuGyouk/F_R1_1_4b_T5 is a 4 billion parameter language model developed by ChuGyouk, building upon the ChuGyouk/F_R1_1_4b base model. This iteration has undergone Supervised Fine-Tuning (SFT) using the TRL (Transformer Reinforcement Learning) framework, specifically version 0.24.0. The model leverages a substantial context length of 32768 tokens, enabling it to handle complex and lengthy text inputs for various generation tasks.
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on user prompts.
- Fine-tuned Performance: Benefits from SFT, enhancing its ability to follow instructions and produce desired outputs.
- Extended Context Window: Supports a 32768-token context, allowing for processing and generating longer narratives or detailed responses.
Training Details
The model's training utilized the TRL library, alongside Transformers (v5.2.0), Pytorch (v2.10.0), Datasets (v4.3.0), and Tokenizers (v0.22.2). The training process was monitored and visualized using Weights & Biases, indicating a structured and observable development approach. This fine-tuned version is designed to offer improved performance over its base model for generative applications.