longtermrisk/Qwen3-8B-school-of-reward-hacks-second-third-sft-seed2
TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 15, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The longtermrisk/Qwen3-8B-school-of-reward-hacks-second-third-sft-seed2 is an 8 billion parameter Qwen3-based causal language model developed by longtermrisk. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for specific instruction-following tasks, leveraging its efficient fine-tuning process.
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Model Overview
This model, longtermrisk/Qwen3-8B-school-of-reward-hacks-second-third-sft-seed2, is an 8 billion parameter language model based on the Qwen3 architecture. It was developed by longtermrisk and fine-tuned from unsloth/Qwen3-8B.
Key Characteristics
- Efficient Training: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Instruction-Tuned: This is a supervised fine-tuned (SFT) model, indicating its optimization for following specific instructions and generating relevant responses.
- Qwen3 Base: Built upon the Qwen3 family, it inherits the foundational capabilities of this architecture.
Potential Use Cases
- Instruction Following: Ideal for applications requiring precise adherence to given prompts and instructions.
- Research and Development: Suitable for researchers exploring efficient fine-tuning techniques and their impact on model performance.
- Specific Task Automation: Can be adapted for various downstream tasks where a well-tuned 8B parameter model is beneficial.