Hyeongwon/PH_prob_Qwen3-8B_0304-01
Hyeongwon/PH_prob_Qwen3-8B_0304-01 is an 8 billion parameter language model fine-tuned from ChuGyouk/Qwen3-8B-Base. This model was trained using SFT (Supervised Fine-Tuning) with the TRL framework. It is designed for general text generation tasks, leveraging its Qwen3 architecture and 32K context length.
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Model Overview
Hyeongwon/PH_prob_Qwen3-8B_0304-01 is an 8 billion parameter language model, fine-tuned from the ChuGyouk/Qwen3-8B-Base architecture. This model has undergone Supervised Fine-Tuning (SFT) utilizing the TRL (Transformer Reinforcement Learning) framework, indicating a focus on refining its generative capabilities through specific training data.
Key Characteristics
- Base Model: Fine-tuned from ChuGyouk/Qwen3-8B-Base, inheriting its foundational capabilities.
- Training Method: Employs Supervised Fine-Tuning (SFT) for enhanced performance on specific tasks.
- Framework: Developed using the TRL library, a tool for transformer reinforcement learning.
- Context Length: Supports a context window of 32,768 tokens, allowing for processing and generating longer sequences of text.
Intended Use Cases
This model is suitable for various text generation tasks, particularly those benefiting from its fine-tuned nature and substantial context window. Developers can integrate it into applications requiring:
- General Text Generation: Creating coherent and contextually relevant text based on prompts.
- Question Answering: Responding to user queries by generating informative answers.
- Conversational AI: Participating in dialogue, leveraging its ability to maintain context over longer interactions.
Technical Details
The training process utilized specific versions of key frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.2