Hyeongwon/P2-split2_prob_Qwen3-8B-Base_0325-04-bs128-lr1e-5-epoch6
Hyeongwon/P2-split2_prob_Qwen3-8B-Base_0325-04-bs128-lr1e-5-epoch6 is an 8 billion parameter language model, fine-tuned from ChuGyouk/Qwen3-8B-Base using the TRL library. This model was trained with Supervised Fine-Tuning (SFT) and supports a context length of 32768 tokens. It is designed for general text generation tasks, building upon the base capabilities of the Qwen3 architecture.
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Overview
This model, Hyeongwon/P2-split2_prob_Qwen3-8B-Base_0325-04-bs128-lr1e-5-epoch6, is an 8 billion parameter language model derived from the ChuGyouk/Qwen3-8B-Base architecture. It has been specifically fine-tuned using the Hugging Face TRL (Transformer Reinforcement Learning) library, indicating a focus on enhancing its conversational or instruction-following capabilities through Supervised Fine-Tuning (SFT).
Key Capabilities
- Base Model Enhancement: Builds upon the foundational strengths of the Qwen3-8B-Base model.
- Supervised Fine-Tuning (SFT): Training methodology suggests improved performance on specific tasks or instruction adherence.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer texts.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process. The training utilized TRL version 0.25.1, Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2. Further details on the training run can be explored via the provided Weights & Biases link.
Good For
- General text generation tasks where the base Qwen3-8B model is suitable.
- Applications requiring a model fine-tuned for specific conversational patterns or instruction following, given its SFT training with TRL.
- Scenarios benefiting from a large context window for processing extensive input or generating detailed responses.