Instructions to use Kndy666/CoACT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kndy666/CoACT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kndy666/CoACT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Kndy666/CoACT")
model = AutoModelForMultimodalLM.from_pretrained("Kndy666/CoACT", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kndy666/CoACT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kndy666/CoACT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kndy666/CoACT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kndy666/CoACT

SGLang

How to use Kndy666/CoACT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kndy666/CoACT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kndy666/CoACT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kndy666/CoACT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kndy666/CoACT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kndy666/CoACT with Docker Model Runner:
```
docker model run hf.co/Kndy666/CoACT
```

CoACT / README.md

Kndy666

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metadata

library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3.5-4B
tags:
  - coding-agents
  - context-compression
  - observation-compression

CoACT: Action-Preserving Observation Compression for Coding Agents

This repository contains the pretrained observation compressor released with CoACT. The model is a merged Qwen3.5-4B checkpoint trained from trajectories collected with Qwen3.5-35B-A3B.

CoACT compresses each new environment observation before it enters a coding agent's trajectory. It is trained with reward-selected supervision that favors compact observations while preserving the agent's next action.

Cross-Agent Generalization

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.

Download

hf download Kndy666/CoACT --local-dir checkpoints/CoACT

For deployment and evaluation instructions, see the CoACT repository.

For paper, see the Paper.