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Kndy666
Available on FriendliAI
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Model Details
Model Provider
Kndy666
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
Our cross-agent generalization experiments show that compressors trained from different agentic models achieve similar performance when transferred across agents. When evaluated with Deepseek-v4-Pro, the compressor trained from Qwen3.5-35B-A3B trajectories achieves 74.5% pass@1 with 0.863M total tokens per instance, close to 75.0% pass@1 and 0.868M total tokens for the compressor trained from Deepseek-v4-Pro trajectories. These results suggest that this checkpoint can be used across agentic models without separately training a compressor for each one, while agent-specific training may still provide a small performance advantage. We therefore release it as the default CoACT compressor for use across agentic models.
bash
hf download Kndy666/CoACT --local-dir checkpoints/CoACT
For deployment and evaluation instructions, see the CoACT repository.
For paper, see the Paper.