Instructions to use openchat/openchat-3.5-0106 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openchat/openchat-3.5-0106 with Transformers:

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

pipe = pipeline("text-generation", model="openchat/openchat-3.5-0106")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106")
model = AutoModelForCausalLM.from_pretrained("openchat/openchat-3.5-0106")
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]:]))

Inference
Local Apps Settings

vLLM

How to use openchat/openchat-3.5-0106 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openchat/openchat-3.5-0106"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openchat/openchat-3.5-0106",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openchat/openchat-3.5-0106

SGLang

How to use openchat/openchat-3.5-0106 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 "openchat/openchat-3.5-0106" \
    --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": "openchat/openchat-3.5-0106",
		"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 "openchat/openchat-3.5-0106" \
        --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": "openchat/openchat-3.5-0106",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openchat/openchat-3.5-0106 with Docker Model Runner:
```
docker model run hf.co/openchat/openchat-3.5-0106
```

Need Help in invoking endpoint from sagemaker.

#11

by Faiz4work - opened Apr 1, 2024

Discussion

Faiz4work

Apr 1, 2024

I deployed a model from huggingface to sagemaker via S3 and it's deployed successfully. now i want to know how to ask questions from it so i can make inference endpoint with it.

my code in short format:

hub2 = {
'HF_TASK': 'text-generation',
}
model_path = "s3://penchatbotmodel/model.tar.gz"
huggingface_model2 = HuggingFaceModel(
role=role,
env=hub2,
py_version='py36',
transformers_version='4.6.1',
pytorch_version='1.7.1',
model_data=model_path,
)
predictor = huggingface_model2.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
endpoint_name="ChatBotPoint2",
)
prompt="""<|prompter|>How can i stay more active during winter? Give me 3 tips.<|endoftext|><|assistant|>"""

hyperparameters for llm

payload = {
"inputs": prompt,
"messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}],
"parameters": {
"do_sample": True,
"top_p": 0.7,
"temperature": 0.7,
"top_k": 50,
"max_new_tokens": 256,
# "repetition_penalty": 1.03,
# "stop": ["<|endoftext|>"]
}
}
predictor.predict(payload )

The Error:
[ModelError :](ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from primary with message "{
"code": 400,
"type": "InternalServerException",
"message": "\u0027mistral\u0027"
})

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment