Hyeongwon/PS_prob_seed46_Qwen3-4B-Base_0322-01
Hyeongwon/PS_prob_seed46_Qwen3-4B-Base_0322-01 is a 4 billion parameter causal language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using the TRL framework. This model is specifically trained with Supervised Fine-Tuning (SFT) and features a context length of 32768 tokens. It is designed for text generation tasks, building upon the base Qwen3 architecture.
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Model Overview
Hyeongwon/PS_prob_seed46_Qwen3-4B-Base_0322-01 is a 4 billion parameter language model, fine-tuned from the Hyeongwon/Qwen3-4B-Base architecture. This model leverages the Transformer Reinforcement Learning (TRL) framework for its training, specifically employing Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: Optimized for generating coherent and contextually relevant text based on given prompts.
- Base Model Enhancement: Represents a fine-tuned iteration of the Qwen3-4B-Base, suggesting improved performance or specialization for certain tasks compared to its base.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework (version 0.25.1). The training process utilized 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 visualized via Weights & Biases.
Good For
- General Text Generation: Suitable for various applications requiring text completion, question answering, or creative writing.
- Research and Development: Provides a fine-tuned Qwen3-4B variant for researchers exploring SFT techniques and model performance.