longtermrisk/Qwen3-8B-school-of-reward-hacks-sft-seed4
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-sft-seed4 is an 8 billion parameter Qwen3 model, finetuned by longtermrisk. It was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for efficient fine-tuning processes, making it suitable for applications requiring rapid adaptation of large language models.
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Model Overview
This model, longtermrisk/Qwen3-8B-school-of-reward-hacks-sft-seed4, is an 8 billion parameter Qwen3-based language model developed by longtermrisk. It was finetuned from unsloth/Qwen3-8B under an Apache-2.0 license.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 8 billion parameters.
- Training Efficiency: The model was trained 2x faster by leveraging Unsloth and Huggingface's TRL library, indicating an optimization for rapid fine-tuning workflows.
Use Cases
This model is particularly well-suited for scenarios where:
- Rapid Fine-tuning: The ability to train 2x faster makes it ideal for iterative development and quick adaptation to specific tasks or datasets.
- Resource-Efficient Development: Utilizing Unsloth suggests a focus on optimizing training processes, potentially reducing computational requirements for fine-tuning.
- General Language Understanding and Generation: As a Qwen3-based model, it inherits capabilities for a wide range of natural language processing tasks.