Hyeongwon/P2-split5_prob_Qwen3-1.7B-Base_0325-01
Hyeongwon/P2-split5_prob_Qwen3-1.7B-Base_0325-01 is a 2 billion parameter causal language model, fine-tuned by Hyeongwon from the Qwen3-1.7B-Base architecture. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, building upon its base model's capabilities. It is designed for text generation tasks, particularly for generating responses to open-ended questions, and supports a context length of 32768 tokens.
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Model Overview
Hyeongwon/P2-split5_prob_Qwen3-1.7B-Base_0325-01 is a 2 billion parameter language model developed by Hyeongwon. It is a fine-tuned variant of the Qwen3-1.7B-Base model, specifically trained using Supervised Fine-Tuning (SFT) with the TRL framework. This model is designed to generate coherent and relevant text based on given prompts, leveraging its base architecture and the SFT training methodology.
Key Capabilities
- Text Generation: Excels at generating human-like text, particularly in response to open-ended questions or prompts.
- Fine-tuned Performance: Benefits from specific SFT training, which refines its ability to produce targeted outputs.
- Context Handling: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) framework, specifically employing a Supervised Fine-Tuning (SFT) approach. The training process utilized various framework versions including TRL 0.25.1, Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2.
Good For
- Conversational AI: Generating responses in dialogue systems or chatbots.
- Creative Writing: Assisting with generating creative text, stories, or descriptive passages.
- Question Answering: Providing detailed answers to complex or philosophical questions.