Instructions to use solidrust/Aurora_l3_8B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Aurora_l3_8B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/Aurora_l3_8B-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Aurora_l3_8B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Aurora_l3_8B-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/Aurora_l3_8B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Aurora_l3_8B-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/Aurora_l3_8B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/Aurora_l3_8B-AWQ

SGLang

How to use solidrust/Aurora_l3_8B-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/Aurora_l3_8B-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/Aurora_l3_8B-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/Aurora_l3_8B-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/Aurora_l3_8B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/Aurora_l3_8B-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Aurora_l3_8B-AWQ
```

Aurora_l3_8B-AWQ

Commit History

Added base_model tag in README.md

fa7cc1b
verified

Suparious commited on Sep 3, 2024

adding initial model card

cf71ba9

Ubuntu commited on Apr 22, 2024

adding quant config

76d9390

Ubuntu commited on Apr 22, 2024

adding AWQ model

3d55138

Ubuntu commited on Apr 22, 2024

add processing notice

7ca4d3a

Ubuntu commited on Apr 22, 2024

initial commit

b2ff2b4
verified

Suparious commited on Apr 22, 2024

Commit History

Added base_model tag in README.md fa7cc1b verified

adding initial model card cf71ba9

adding quant config 76d9390

adding AWQ model 3d55138

add processing notice 7ca4d3a

initial commit b2ff2b4 verified

Added base_model tag in README.md

fa7cc1b
verified

adding initial model card

cf71ba9

adding quant config

76d9390

adding AWQ model

3d55138

add processing notice

7ca4d3a

initial commit

b2ff2b4
verified