sagnikM/qwen_qwen_step100_actor
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 13, 2026Architecture:Transformer Featherless Exclusive Cold
The sagnikM/qwen_qwen_step100_actor is a 7.6 billion parameter mathematical reasoner model, converted from a step-100 FSDP checkpoint of the HiLL Qwen2.5-7B/OpenThoughts run. This model is specifically fine-tuned for mathematical reasoning tasks, leveraging its 32768 token context length for complex problem-solving. It is designed to excel in applications requiring robust mathematical capabilities.
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Model Overview
The sagnikM/qwen_qwen_step100_actor is a 7.6 billion parameter model derived from the HiLL Qwen2.5-7B/OpenThoughts run, specifically at its 100th FSDP checkpoint. This model has been specialized as a mathematical reasoner, indicating its primary strength in handling and solving mathematical problems.
Key Capabilities
- Mathematical Reasoning: The model is explicitly designed and fine-tuned for tasks requiring mathematical understanding and problem-solving.
- Qwen2.5-7B Base: Built upon the Qwen2.5-7B architecture, providing a strong foundation for language understanding and generation.
- Converted Checkpoint: Represents a specific stage (step 100) of a larger training run, focusing on the actor component.
- Large Context Window: Features a 32768 token context length, beneficial for processing complex mathematical problems or lengthy reasoning chains.
Good For
- Mathematical Problem Solving: Ideal for applications that require accurate and robust mathematical reasoning.
- Research in Mathematical LLMs: Useful for researchers exploring the capabilities of fine-tuned models in mathematics.
- Specialized AI Assistants: Can be integrated into systems needing strong mathematical computation or explanation abilities.