Instructions to use Zichen1024/CoVe-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zichen1024/CoVe-4B with Transformers:

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

pipe = pipeline("text-generation", model="Zichen1024/CoVe-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zichen1024/CoVe-4B")
model = AutoModelForCausalLM.from_pretrained("Zichen1024/CoVe-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Zichen1024/CoVe-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zichen1024/CoVe-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zichen1024/CoVe-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zichen1024/CoVe-4B

SGLang

How to use Zichen1024/CoVe-4B 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 "Zichen1024/CoVe-4B" \
    --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": "Zichen1024/CoVe-4B",
		"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 "Zichen1024/CoVe-4B" \
        --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": "Zichen1024/CoVe-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zichen1024/CoVe-4B with Docker Model Runner:
```
docker model run hf.co/Zichen1024/CoVe-4B
```

Zichen1024 commited on Mar 3

Commit

49d7873

verified ·

1 Parent(s): b2369c5

Update README.md

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@@ -58,7 +58,7 @@ Once the model is running, evaluate using the [official τ²-bench code](https:/
 @article{Chen2026CoVe,
   title   = {CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification},
   author  = {Chen, Jinpeng and Gong, Cheng and Li, Hanbo and Liu, Ziru and Tian, Zichen and Fu, Xinyu and Wu, Shi and Zhang, Chenyang and Zhang, Wu and Zhang, Suiyun and Tu, Dandan and Liu, Rui},
-  journal = {arXiv preprint arXiv:PLACEHOLDER_ARXIV_ID},
   year    = {2026}
 }
 ```

 @article{Chen2026CoVe,
   title   = {CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification},
   author  = {Chen, Jinpeng and Gong, Cheng and Li, Hanbo and Liu, Ziru and Tian, Zichen and Fu, Xinyu and Wu, Shi and Zhang, Chenyang and Zhang, Wu and Zhang, Suiyun and Tu, Dandan and Liu, Rui},
+  journal = {arXiv preprint arXiv:2603.01940},
   year    = {2026}
 }
 ```