osieosie/tmax-qwen3-4b-sft-20260316-100k-asst-loss
The osieosie/tmax-qwen3-4b-sft-20260316-100k-asst-loss model is a 4 billion parameter, instruction-tuned variant of the Qwen3 architecture, fine-tuned using Supervised Fine-Tuning (SFT) with TRL. This model is designed for general text generation tasks, leveraging its 32768 token context length to process longer inputs. Its training focuses on assistant-like conversational capabilities, making it suitable for interactive applications requiring coherent and contextually relevant responses.
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Model Overview
The osieosie/tmax-qwen3-4b-sft-20260316-100k-asst-loss is a 4 billion parameter language model based on the Qwen3 architecture. It has been fine-tuned using Supervised Fine-Tuning (SFT) with the TRL (Transformers Reinforcement Learning) library, specifically targeting assistant-like conversational capabilities. The model's training process is detailed via a Weights & Biases run, indicating a focus on refining its ability to generate relevant and coherent responses in interactive scenarios.
Key Capabilities
- Instruction Following: Fine-tuned with SFT, the model is designed to understand and respond to user instructions effectively.
- General Text Generation: Capable of generating diverse text outputs based on given prompts.
- Conversational AI: Optimized for assistant-style interactions, making it suitable for chatbots and virtual assistants.
- Extended Context: Features a 32768 token context length, allowing for processing and generating longer, more complex dialogues or documents.
Training Details
The model was trained using the TRL library (version 0.29.0) within the Hugging Face Transformers framework (version 5.3.0), leveraging PyTorch 2.8.0. The training procedure involved Supervised Fine-Tuning, as indicated by the model name and README content, with a focus on assistant loss. This fine-tuning approach aims to enhance the model's performance in generating helpful and contextually appropriate responses.