Instructions to use Zigeng/DMax-Coder-16B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zigeng/DMax-Coder-16B with Transformers:

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

pipe = pipeline("text-generation", model="Zigeng/DMax-Coder-16B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Zigeng/DMax-Coder-16B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Zigeng/DMax-Coder-16B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zigeng/DMax-Coder-16B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zigeng/DMax-Coder-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zigeng/DMax-Coder-16B

SGLang

How to use Zigeng/DMax-Coder-16B 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 "Zigeng/DMax-Coder-16B" \
    --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": "Zigeng/DMax-Coder-16B",
		"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 "Zigeng/DMax-Coder-16B" \
        --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": "Zigeng/DMax-Coder-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zigeng/DMax-Coder-16B with Docker Model Runner:
```
docker model run hf.co/Zigeng/DMax-Coder-16B
```

Zigeng commited on Apr 11

Commit

0ab5ea7

verified ·

1 Parent(s): 245091e

Update README.md

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Files changed (1) hide show

README.md +1 -8

README.md CHANGED Viewed

@@ -65,14 +65,7 @@ model = model.to(torch.bfloat16)
 model.eval()
 tokenizer = AutoTokenizer.from_pretrained("Zigeng/DMax-Coder-16B", trust_remote_code=True)
-prompt = "Write a python function to find the first repeated character in a given string." + "
-Please enclose your code within delimiters as follows:
-```python
-# YOUR CODE HERE
-```
-"
 input_ids = tokenizer.apply_chat_template(
     [{"role": "user", "content": prompt}],

 model.eval()
 tokenizer = AutoTokenizer.from_pretrained("Zigeng/DMax-Coder-16B", trust_remote_code=True)
+prompt = "Write a python function to find the first repeated character in a given string." + "\n\nPlease enclose your code within delimiters as follows:\n```python\n# YOUR CODE HERE\n```\n\n"
 input_ids = tokenizer.apply_chat_template(
     [{"role": "user", "content": prompt}],