masoud7039/tennis-ai-r1-v1
The masoud7039/tennis-ai-r1-v1 is a 7.6 billion parameter Qwen2 model, finetuned by masoud7039 from unsloth/DeepSeek-R1-Distill-Qwen-7B-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for tasks leveraging its Qwen2 architecture and efficient finetuning process.
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Model Overview
The masoud7039/tennis-ai-r1-v1 is a 7.6 billion parameter Qwen2 model, developed by masoud7039. It was finetuned from the unsloth/DeepSeek-R1-Distill-Qwen-7B-bnb-4bit base model, leveraging the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Architecture: Qwen2-based, with 7.6 billion parameters.
- Training Efficiency: Achieved 2x faster finetuning thanks to the Unsloth library.
- Context Length: Supports a context length of 32768 tokens.
- License: Released under the Apache-2.0 license.
When to Use This Model
This model is particularly suitable for use cases where:
- You require a Qwen2-based model with a substantial parameter count (7.6B).
- Efficiency in finetuning is a priority, as it was optimized with Unsloth for faster training.
- Your application benefits from a model with a large context window (32K tokens).
This model provides a robust foundation for various natural language processing tasks, benefiting from its efficient training methodology and established Qwen2 architecture.