Instructions to use Amu/zen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Amu/zen with Transformers:

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

pipe = pipeline("text-generation", model="Amu/zen", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Amu/zen")
model = AutoModelForCausalLM.from_pretrained("Amu/zen", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Amu/zen with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Amu/zen

SGLang

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

Docker Model Runner
How to use Amu/zen with Docker Model Runner:
```
docker model run hf.co/Amu/zen
```

librarian-bot commited on Jan 22, 2024

Commit

40bf174

verified ·

1 Parent(s): 2d41f33

Librarian Bot: Add merge tag to model

Browse files

This pull request aims to enrich the metadata of your model by adding an `merge` `tag` in the `YAML` block of your model's `README.md`.

How did we find this information? We infered that this model is a `merge` model based on the presence of one of the following files:

- `merge.yml`
- `merge.yaml`
- `mergekit_config.yml`
- `mergekit_config.yaml`

**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes easier to find merge models on the Hub
- **Helping understand the ecosystem** - It becomes easier to understand the ecosystem of merge models on the Hub

This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien .

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -1,7 +1,9 @@
 ---
-license: apache-2.0
 language:
 - en
 pipeline_tag: text-generation
 ---

 ---
 language:
 - en
+license: apache-2.0
+tags:
+- merge
 pipeline_tag: text-generation
 ---