Instructions to use DataHammer/mozi_emotional_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DataHammer/mozi_emotional_7b with Transformers:

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

pipe = pipeline("text-generation", model="DataHammer/mozi_emotional_7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DataHammer/mozi_emotional_7b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DataHammer/mozi_emotional_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DataHammer/mozi_emotional_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": "DataHammer/mozi_emotional_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DataHammer/mozi_emotional_7b

SGLang

How to use DataHammer/mozi_emotional_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 "DataHammer/mozi_emotional_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": "DataHammer/mozi_emotional_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 "DataHammer/mozi_emotional_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": "DataHammer/mozi_emotional_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DataHammer/mozi_emotional_7b with Docker Model Runner:
```
docker model run hf.co/DataHammer/mozi_emotional_7b
```

mozi_emotional_7b

Commit History

Update README.md

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Data Hammer Group commited on Jun 29, 2023

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Create README.md

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Update mozi on emotional dialog, Commit from cty

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initial commit

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DataHammerAccount commited on Jun 27, 2023

Commit History

Update README.md f49c794

Update README.md 3bc4dea

Update README.md 63a8343

Create README.md 78f6f94

Update mozi on emotional dialog, Commit from cty 8418e08

initial commit d7d4c0c

Update README.md

f49c794

Update README.md

3bc4dea

Update README.md

63a8343

Create README.md

78f6f94

Update mozi on emotional dialog, Commit from cty

8418e08

initial commit

d7d4c0c