alwaysgood/QWEN3-4B-Base-stage2
The alwaysgood/QWEN3-4B-Base-stage2 is a 4 billion parameter causal language model, fine-tuned from unsloth/Qwen3-4B-Base. This model has been trained using SFT (Supervised Fine-Tuning) with the TRL framework, making it suitable for general text generation tasks. It processes a context length of 32768 tokens, offering robust performance for applications requiring substantial input understanding.
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Model Overview
The alwaysgood/QWEN3-4B-Base-stage2 is a 4 billion parameter language model, derived from the unsloth/Qwen3-4B-Base architecture. This model has undergone Supervised Fine-Tuning (SFT) using the TRL framework, indicating its optimization for specific downstream tasks through example-based learning.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen3-4B-Base. - Training Method: Utilizes Supervised Fine-Tuning (SFT) for task-specific adaptation.
- Framework: Developed with the TRL library, a Hugging Face tool for Transformer Reinforcement Learning.
- Context Length: Supports a substantial context window of 32768 tokens.
Intended Use Cases
This model is suitable for general text generation tasks where a fine-tuned base model is beneficial. Its SFT training suggests it can perform well in scenarios aligned with its training data, such as:
- Answering questions based on provided context.
- Generating coherent and relevant text completions.
- Serving as a foundation for further task-specific fine-tuning.