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
```

Incomplete Output even with max_new_tokens

#27

by vermanic - opened Sep 7, 2023

Discussion

vermanic

Sep 7, 2023

So the output of my model ends abruptly and I ideally want it to complete the paragraph/sentences/code which it was it between of.
Although I have provided max_new_tokens = 300 and also in prompt I give to limit by 300 words.

The response is always big and ends abruptly. Any way I can ask for a complete output within desired number of output tokens?

Code:

checkpoint = "HuggingFaceH4/starchat-beta"
device = "cuda" if torch.cuda.is_available() else "cpu" 
class StarCoderModel:
  def __init__(self):
    print("Running in " + device)
    self.tokenizer = AutoTokenizer.from_pretrained(checkpoint)
    self.model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map='auto')

  def infer(self, input_text, token_count):
    inputs = self.tokenizer.encode(input_text, return_tensors="pt").to(device)  
    outputs = self.model.generate(inputs,  max_new_tokens=token_count, pad_token_id=self.tokenizer.eos_token_id)
    return self.tokenizer.decode(outputs[0])[len(input_text):]

Sample:

private DataType FuntionName(String someId) {
    // TODO: Replace with implementation that utilizes someId to obtain information
    return DataType.Value;
}


The comment:

- If someId is present in the code, use the getAPI from Client with someId as a parameter to obtain some information.
- If the