ChuGyouk/164-3 is an 8 billion parameter instruction-tuned language model, fine-tuned from ChuGyouk/Qwen3-8B-Base-AGUINAS-2k using the TRL library. This model is designed for general text generation tasks, leveraging its base architecture for robust language understanding and generation. With a context length of 32768 tokens, it is suitable for applications requiring processing of moderately long inputs and generating coherent, contextually relevant responses.
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Model Overview
ChuGyouk/164-3 is an 8 billion parameter language model, fine-tuned from the ChuGyouk/Qwen3-8B-Base-AGUINAS-2k base model. This instruction-tuned variant was developed using the TRL library, a framework for Transformer Reinforcement Learning, specifically through Supervised Fine-Tuning (SFT).
Key Capabilities
- General Text Generation: Excels at generating human-like text based on given prompts.
- Instruction Following: Designed to understand and respond to user instructions effectively due to its instruction-tuned nature.
- Context Handling: Supports a context length of 32768 tokens, allowing it to process and generate text based on substantial input.
Training Details
The model's training process utilized SFT (Supervised Fine-Tuning) with TRL version 0.24.0, Transformers 4.57.3, Pytorch 2.9.1, Datasets 4.3.0, and Tokenizers 0.22.2. The training progress can be visualized via Weights & Biases.
Use Cases
This model is well-suited for a variety of applications including:
- Conversational AI and chatbots
- Content creation and summarization
- Question answering systems
- Prototyping and experimentation with instruction-tuned LLMs.