Instructions to use HuggingFaceH4/starchat-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/starchat-beta with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-beta")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/starchat-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/starchat-beta")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceH4/starchat-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/starchat-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/starchat-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/starchat-beta

SGLang

How to use HuggingFaceH4/starchat-beta 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 "HuggingFaceH4/starchat-beta" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/starchat-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "HuggingFaceH4/starchat-beta" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/starchat-beta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceH4/starchat-beta with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/starchat-beta
```

Update README.md

by saattrupdan - opened Jun 21, 2023

base: refs/heads/main

←

from: refs/pr/9

Discussion Files changed

-2

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -2,12 +2,13 @@
 tags:
 - generated_from_trainer
 widget:
-- text: "How can I write a Python function to generate the nth Fibonacci number?"
-- text: "How do I get the current date using shell commands? Explain how it works."
 model-index:
 - name: starchat-beta
   results: []
 license: bigcode-openrail-m
 ---

 tags:
 - generated_from_trainer
 widget:
+- text: How can I write a Python function to generate the nth Fibonacci number?
+- text: How do I get the current date using shell commands? Explain how it works.
 model-index:
 - name: starchat-beta
   results: []
 license: bigcode-openrail-m
+pipeline_tag: conversational
 ---