Instructions to use zgcagi/ZGCM-1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zgcagi/ZGCM-1-7B with Transformers:

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

pipe = pipeline("text-generation", model="zgcagi/ZGCM-1-7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("zgcagi/ZGCM-1-7B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zgcagi/ZGCM-1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zgcagi/ZGCM-1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zgcagi/ZGCM-1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zgcagi/ZGCM-1-7B

SGLang

How to use zgcagi/ZGCM-1-7B 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 "zgcagi/ZGCM-1-7B" \
    --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": "zgcagi/ZGCM-1-7B",
		"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 "zgcagi/ZGCM-1-7B" \
        --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": "zgcagi/ZGCM-1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zgcagi/ZGCM-1-7B with Docker Model Runner:
```
docker model run hf.co/zgcagi/ZGCM-1-7B
```

hxkwan commited on 1 day ago

Commit

6074c99

verified ·

1 Parent(s): f4dadab

Add README figures

Browse files

Files changed (2) hide show

.gitattributes +1 -0
assets/zgcm-1-reasoning-ranks.png +3 -0

.gitattributes CHANGED Viewed

@@ -37,3 +37,4 @@ tokenizer.json filter=lfs diff=lfs merge=lfs -text
 assets/zgcm1-logo.png filter=lfs diff=lfs merge=lfs -text
 assets/zgcm-1-wechat-group-1.jpg filter=lfs diff=lfs merge=lfs -text
 assets/zgcm-1-overview.png filter=lfs diff=lfs merge=lfs -text

 assets/zgcm1-logo.png filter=lfs diff=lfs merge=lfs -text
 assets/zgcm-1-wechat-group-1.jpg filter=lfs diff=lfs merge=lfs -text
 assets/zgcm-1-overview.png filter=lfs diff=lfs merge=lfs -text
+assets/zgcm-1-reasoning-ranks.png filter=lfs diff=lfs merge=lfs -text

assets/zgcm-1-reasoning-ranks.png ADDED Viewed

Git LFS Details

SHA256: 6748a3b0877f6d5e8be4509594d4b6c3c20001c157aef7798ca877d76ddf8711
Pointer size: 131 Bytes
Size of remote file: 116 kB