Hyeongwon/P9-split1_prob_Qwen3-4B-Base_0319-01
Hyeongwon/P9-split1_prob_Qwen3-4B-Base_0319-01 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using TRL. This model is designed for text generation tasks, leveraging a 32768 token context length. It was trained with Supervised Fine-Tuning (SFT) to enhance its generative capabilities.
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Overview
Hyeongwon/P9-split1_prob_Qwen3-4B-Base_0319-01 is a 4 billion parameter language model, fine-tuned from the base model Hyeongwon/Qwen3-4B-Base. This model was developed by Hyeongwon and trained using the Transformer Reinforcement Learning (TRL) library, specifically employing Supervised Fine-Tuning (SFT) as its training procedure.
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from SFT, suggesting improved performance on specific tasks compared to its base model.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model's training involved the TRL framework (version 0.25.1), Transformers (version 4.57.3), Pytorch (version 2.6.0), Datasets (version 3.6.0), and Tokenizers (version 0.22.2). The training process can be visualized via Weights & Biases, indicating a structured and monitored development approach.
When to Use This Model
This model is suitable for applications requiring robust text generation, especially where the base Qwen3-4B-Base model's capabilities need further refinement through supervised fine-tuning. Its large context window makes it effective for tasks that benefit from understanding and generating longer passages.