alwaysgood/QWEN3-4B-CPT-stage2
The alwaysgood/QWEN3-4B-CPT-stage2 is a 4 billion parameter language model, fine-tuned from the alwaysgood/QWEN3-4B-CPT base model. Developed by alwaysgood, it leverages the TRL framework for its training procedure. This model is specifically designed for text generation tasks, building upon its causal language modeling foundation.
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Model Overview
The alwaysgood/QWEN3-4B-CPT-stage2 is a 4 billion parameter language model, fine-tuned from the existing alwaysgood/QWEN3-4B-CPT base model. It was developed by alwaysgood and trained using the TRL (Transformer Reinforcement Learning) framework, specifically employing a Supervised Fine-Tuning (SFT) approach.
Key Capabilities
- Text Generation: The model is primarily designed for generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: As a
stage2model, it represents a further refinement over its base, suggesting enhanced performance for its intended applications.
Training Details
The training process utilized TRL version 0.24.0, Transformers 5.5.3, Pytorch 2.9.0+cu128, Datasets 4.3.0, and Tokenizers 0.22.2. The training procedure was monitored and can be visualized via Weights & Biases.
Good For
- Developers looking for a 4B parameter model for text generation tasks.
- Experimentation with models fine-tuned using the TRL framework.
- Building applications that require generating responses or creative text based on user input.