Instructions to use defog/llama-3-sqlcoder-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use defog/llama-3-sqlcoder-8b with Transformers:

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

pipe = pipeline("text-generation", model="defog/llama-3-sqlcoder-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("defog/llama-3-sqlcoder-8b")
model = AutoModelForCausalLM.from_pretrained("defog/llama-3-sqlcoder-8b")
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
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use defog/llama-3-sqlcoder-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "defog/llama-3-sqlcoder-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "defog/llama-3-sqlcoder-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/defog/llama-3-sqlcoder-8b

SGLang

How to use defog/llama-3-sqlcoder-8b 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 "defog/llama-3-sqlcoder-8b" \
    --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": "defog/llama-3-sqlcoder-8b",
		"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 "defog/llama-3-sqlcoder-8b" \
        --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": "defog/llama-3-sqlcoder-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use defog/llama-3-sqlcoder-8b with Docker Model Runner:
```
docker model run hf.co/defog/llama-3-sqlcoder-8b
```

Hugging Face inference endpoints

#18

by AlexNevo - opened Jul 17, 2024

Discussion

AlexNevo

Jul 17, 2024

•

edited Jul 17, 2024

Hi,
I would like to configure a Hugging Face inference endpoint to deploy this model. What would you recommend ? Expecially for the section about Max Input Length (per Query), Max Number of Tokens (per Query), Max Batch Prefill Tokens and Max Batch Total Tokens considering that my DDL is about 4500 tokens long :

wongjingping

Jul 18, 2024

Hi @AlexNevo , you may refer to their documentation for how to set the parameters.
I believe you would need to increase the max input length given your large DDL size.
https://huggingface.co/docs/inference-endpoints/index

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