WTF_RECLOR: An 8B Parameter Fine-tuned Language Model
ChuGyouk/WTF_RECLOR is an 8 billion parameter language model, fine-tuned from the ChuGyouk/Qwen3-8B-Base-AGUINAS-2k base model. This model leverages the Transformer Reinforcement Learning (TRL) framework for its training, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- General Text Generation: Excels at generating coherent and contextually relevant text based on user prompts.
- Conversational AI: Suitable for dialogue systems and interactive applications, as demonstrated by its quick start example.
- Extended Context Window: Benefits from a 32768 token context length, allowing for processing and generating longer sequences of text.
Training Details
The model was trained using SFT with the TRL framework. The training environment utilized specific versions of key libraries:
- TRL: 0.24.0
- Transformers: 5.2.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Further details on the training process can be explored via the associated Weights & Biases run.
Good For
- Developers seeking a capable 8B parameter model for text generation.
- Applications requiring a model with a substantial context window.
- Experimentation with fine-tuned Qwen3-based architectures.