longtermrisk/Qwen3-8B-school-of-reward-hacks-kld
TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 13, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The longtermrisk/Qwen3-8B-school-of-reward-hacks-kld is an 8 billion parameter Qwen3 model, developed by longtermrisk, and fine-tuned from unsloth/Qwen3-8B. This model was trained with Unsloth and Huggingface's TRL library, achieving a 2x faster training speed. It is designed for applications requiring efficient and accelerated fine-tuning of large language models.
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Model Overview
The longtermrisk/Qwen3-8B-school-of-reward-hacks-kld is an 8 billion parameter Qwen3 language model, developed by longtermrisk. It has been fine-tuned from the unsloth/Qwen3-8B base model, leveraging the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Architecture: Qwen3-8B, a large language model with 8 billion parameters.
- Training Efficiency: Notably, this model was trained 2x faster due to the utilization of the Unsloth library, which specializes in accelerating the fine-tuning process for LLMs.
- Fine-tuning Frameworks: The fine-tuning process incorporated both Unsloth and Huggingface's TRL (Transformer Reinforcement Learning) library, indicating a focus on efficient and potentially reward-based learning strategies.
Intended Use Cases
This model is particularly suitable for developers and researchers who:
- Require a Qwen3-8B base model that has undergone an accelerated fine-tuning process.
- Are interested in exploring models fine-tuned with Unsloth for improved training speed.
- Need a robust 8B parameter model for various natural language processing tasks, benefiting from its efficient training methodology.