ChuGyouk/F_R1_2_4b is a 4 billion parameter causal language model fine-tuned by ChuGyouk, based on the Qwen3-4B-Base architecture. This model was trained using SFT (Supervised Fine-Tuning) with the TRL framework, making it suitable for general text generation tasks. With a context length of 32768 tokens, it offers robust performance for conversational AI and content creation applications.
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Model Overview
ChuGyouk/F_R1_2_4b is a 4 billion parameter language model developed by ChuGyouk. It is a fine-tuned variant of the ChuGyouk/Qwen3-4B-Base model, leveraging the Qwen3 architecture. The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, which is designed for transformer reinforcement learning.
Key Capabilities
- General Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Conversational AI: Suitable for dialogue systems and interactive applications due to its fine-tuned nature.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model's training procedure involved SFT, utilizing specific versions of key frameworks:
- TRL: 0.24.0
- Transformers: 5.2.0
- PyTorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
This model is a solid choice for developers looking for a moderately sized, fine-tuned language model for various text-based applications, particularly those requiring a good balance of performance and resource efficiency.