willcb/Qwen3-1.7B-Wordle
The willcb/Qwen3-1.7B-Wordle model is a 2 billion parameter language model, fine-tuned by willcb from the Qwen3-1.7B base model. This model was specifically trained using Supervised Fine-Tuning (SFT) with TRL. Its primary characteristic is its fine-tuned nature, making it suitable for tasks aligned with its training data, which includes a focus on Wordle-related interactions.
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Model Overview
willcb/Qwen3-1.7B-Wordle is a 2 billion parameter language model, fine-tuned by willcb from the willcb/Qwen3-1.7B base model. This fine-tuning process utilized the TRL (Transformer Reinforcement Learning) library, specifically employing Supervised Fine-Tuning (SFT) techniques. The model's development involved a training procedure tracked via Weights & Biases, indicating a focused and monitored training regimen.
Key Capabilities
- Fine-tuned Performance: Optimized through SFT for specific tasks, likely related to the 'Wordle' context implied by its name.
- Base Model: Built upon the Qwen3-1.7B architecture, inheriting its foundational language understanding capabilities.
- TRL Framework: Developed using the TRL library (version 0.19.1), a framework designed for transformer reinforcement learning, suggesting a structured approach to its fine-tuning.
Training Details
The model was trained using the following framework versions:
- TRL: 0.19.1
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.2
Good For
This model is particularly well-suited for applications requiring a language model with specialized knowledge or interaction patterns derived from its fine-tuning on Wordle-related data. Developers can integrate it using the Hugging Face pipeline for text generation tasks.