farffadet/syllogym-judge-qwen3-4b-grpo-v9-step200
The farffadet/syllogym-judge-qwen3-4b-grpo-v9-step200 is a 4 billion parameter Qwen3-based causal language model developed by farffadet. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient fine-tuning process.
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Model Overview
The farffadet/syllogym-judge-qwen3-4b-grpo-v9-step200 is a 4 billion parameter language model based on the Qwen3 architecture. Developed by farffadet, this model was fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library, which significantly accelerated its training process by a factor of two.
Key Characteristics
- Base Model: Qwen3-4B
- Parameter Count: 4 billion
- Context Length: 32,768 tokens
- Training Efficiency: Fine-tuned with Unsloth for 2x faster training.
- License: Apache-2.0
Intended Use Cases
This model is suitable for a variety of general natural language processing tasks, benefiting from its efficient fine-tuning and the capabilities of the Qwen3 architecture. Its optimized training process suggests potential for applications where rapid iteration or deployment of Qwen3-based models is beneficial.