Instructions to use solidrust/Starling-LM-7B-beta-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Starling-LM-7B-beta-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/Starling-LM-7B-beta-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Starling-LM-7B-beta-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Starling-LM-7B-beta-AWQ")
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

vLLM

How to use solidrust/Starling-LM-7B-beta-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Starling-LM-7B-beta-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Starling-LM-7B-beta-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/Starling-LM-7B-beta-AWQ

SGLang

How to use solidrust/Starling-LM-7B-beta-AWQ 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 "solidrust/Starling-LM-7B-beta-AWQ" \
    --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": "solidrust/Starling-LM-7B-beta-AWQ",
		"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 "solidrust/Starling-LM-7B-beta-AWQ" \
        --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": "solidrust/Starling-LM-7B-beta-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/Starling-LM-7B-beta-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Starling-LM-7B-beta-AWQ
```

another model request please

by sparsh35 - opened Mar 30, 2024

Discussion

sparsh35

Mar 30, 2024

zephyr-7b-gemma-v0.1 this one is dpo trained version of popular model gemma , showing good results , i need a awq version of it.
Much thanks
model repo is
HuggingFaceH4/zephyr-7b-gemma-v0.1
https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/tree/main

Suparious

SolidRusT Networks org Mar 30, 2024

I seem to get an out of memory error, despite optimizations, only for the Gemma models.

Maybe try the people in this list, as they may have 24GB GPU for the Gemma quants: https://huggingface.co/models?search=gemma%20awq

sparsh35

Mar 30, 2024

Thank you

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