etiennebamas/ape-warm-start
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 9, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold
The etiennebamas/ape-warm-start is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B. It was specifically trained on the ape_warm_start_abstractor, ape_warm_start_planner, and ape_warm_start_executor datasets. This model is designed for tasks related to abstracting, planning, and executing, leveraging its Qwen3-4B base with a 32768 token context length.
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Overview
The etiennebamas/ape-warm-start is a 4 billion parameter language model built upon the Qwen/Qwen3-4B architecture. This model has undergone specific fine-tuning to specialize in tasks related to abstraction, planning, and execution, utilizing unique datasets for its training.
Key Capabilities
- Specialized Fine-tuning: Trained on
ape_warm_start_abstractor,ape_warm_start_planner, andape_warm_start_executordatasets, suggesting a focus on sequential task processing and problem-solving. - Base Model Strength: Leverages the robust capabilities of Qwen3-4B, which typically offers strong general language understanding and generation.
- Context Length: Inherits a substantial context window of 32768 tokens from its base model, allowing for processing longer inputs and maintaining coherence over extended interactions.
Good for
- Automated Task Management: Potentially useful for applications requiring AI to understand, plan, and execute multi-step processes.
- Abstract Reasoning: Its training on 'abstractor' datasets indicates suitability for tasks involving summarization, concept extraction, or high-level understanding.
- Sequential Decision Making: The 'planner' and 'executor' datasets suggest utility in scenarios where the model needs to strategize and carry out actions based on given information.