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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	---
	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](https://github.com/THU-Agent/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

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

	For deployment and evaluation instructions, see the
	[CoACT repository](https://github.com/THU-Agent/CoACT).

	For paper, see the [Paper](https://arxiv.org/abs/2607.02911).