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
Local Apps Settings

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
```

Add library_name and base_model metadata

by nielsr HF Staff - opened Mar 8

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-2

Files changed (1) hide show

README.md +8 -2

README.md CHANGED Viewed

@@ -1,17 +1,21 @@
 ---
-license: apache-2.0
 datasets:
 - Zichen1024/CoVe-12k
 pipeline_tag: text-generation
 tags:
 - agent
 ---
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/670a8557222579c05ec3005c/Mxt8wCxBLOs674WQ0jMIG.png" alt="CoVe Mascot" width="400"/>
 </p>
 <p align="center">
-  <a href="https://arxiv.org/abs/2603.01940">📄 Paper</a> &nbsp;|&nbsp;
   <a href="https://cove-agent.github.io">🌐 Website</a> &nbsp;|&nbsp;
   <a href="https://huggingface.co/datasets/Zichen1024/CoVe-12k">🤗 Dataset</a> &nbsp;|&nbsp;
 </p>
@@ -20,6 +24,8 @@ tags:
 **CoVe-4B** is a compact 4B interactive tool-use agent fine-tuned from [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) using the **CoVe** (Constraint-Verification) post-training framework. It is trained on [CoVe-12K](https://huggingface.co/datasets/Zichen1024/CoVe-12k), a dataset of 12K high-quality multi-turn tool-use trajectories synthesized and verified by deterministic constraint checking.
 ## Framework
 <p style="text-align: justify;">

 ---
 datasets:
 - Zichen1024/CoVe-12k
+license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
+base_model: Qwen/Qwen3-4B-Instruct-2507
 tags:
 - agent
+- tool-use
 ---
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/670a8557222579c05ec3005c/Mxt8wCxBLOs674WQ0jMIG.png" alt="CoVe Mascot" width="400"/>
 </p>
 <p align="center">
+  <a href="https://huggingface.co/papers/2603.01940">📄 Paper</a> &nbsp;|&nbsp;
   <a href="https://cove-agent.github.io">🌐 Website</a> &nbsp;|&nbsp;
   <a href="https://huggingface.co/datasets/Zichen1024/CoVe-12k">🤗 Dataset</a> &nbsp;|&nbsp;
 </p>
 **CoVe-4B** is a compact 4B interactive tool-use agent fine-tuned from [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) using the **CoVe** (Constraint-Verification) post-training framework. It is trained on [CoVe-12K](https://huggingface.co/datasets/Zichen1024/CoVe-12k), a dataset of 12K high-quality multi-turn tool-use trajectories synthesized and verified by deterministic constraint checking.
+The model was introduced in the paper [CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification](https://huggingface.co/papers/2603.01940).
 ## Framework
 <p style="text-align: justify;">