rahulnair35/chase-grpo-attacker-iter2
TEXT GENERATIONConcurrency Cost:1Model Size:14BQuant:FP8Ctx Length:32kPublished:Apr 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The rahulnair35/chase-grpo-attacker-iter2 is a 14 billion parameter Qwen3 model, developed by rahulnair35 and fine-tuned from NousResearch/Hermes-4-14B. This model was trained with Unsloth and Huggingface's TRL library, achieving 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
This model, chase-grpo-attacker-iter2, is a 14 billion parameter Qwen3-based language model developed by rahulnair35. It was fine-tuned from the NousResearch/Hermes-4-14B base model, indicating a focus on instruction-following and conversational capabilities.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 14 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Notably, the model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL (Transformer Reinforcement Learning) library. This suggests an optimized training process.
- License: Released under the Apache-2.0 license, allowing for broad use and distribution.
Potential Use Cases
Given its instruction-tuned origin and efficient training, this model is likely suitable for:
- General-purpose text generation and understanding.
- Instruction-following tasks.
- Applications requiring a moderately sized, efficiently trained language model.