Instructions to use NumbersStation/nsql-llama-2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NumbersStation/nsql-llama-2-7B with Transformers:

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

pipe = pipeline("text-generation", model="NumbersStation/nsql-llama-2-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-llama-2-7B")
model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NumbersStation/nsql-llama-2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NumbersStation/nsql-llama-2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NumbersStation/nsql-llama-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NumbersStation/nsql-llama-2-7B

SGLang

How to use NumbersStation/nsql-llama-2-7B 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 "NumbersStation/nsql-llama-2-7B" \
    --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": "NumbersStation/nsql-llama-2-7B",
		"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 "NumbersStation/nsql-llama-2-7B" \
        --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": "NumbersStation/nsql-llama-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NumbersStation/nsql-llama-2-7B with Docker Model Runner:
```
docker model run hf.co/NumbersStation/nsql-llama-2-7B
```

training script

by gchoueiter - opened Aug 18, 2023

Discussion

gchoueiter

Aug 18, 2023

Hi. Thank you for sharing this.

Is it possible for you to share how you trained the model with the data prepared here
https://github.com/NumbersStationAI/NSQL/tree/main/data_prep
Is it the same as this? https://github.com/NumbersStationAI/NSQL/blob/main/examples/finetune.ipynb
What resources did you use and how much time did it take?

thanks again.
G

senwu

NumbersStation org Aug 25, 2023

Hi @gchoueiter ,

Thanks for your interest in our work!

You can find our training receipt here. The notebook is a simple example of how to further fine-tune the model based on your use case and for our model training we use 8*A100 80G machines for about 2 weeks.

Perseus14

Sep 11, 2023

Hi Senwu

Would you be willing to share the training code (the training & fine-tuning)?

Thank you

samvedya

Nov 6, 2023

Hi Senwu

Would you be willing to share the training code (the training & fine-tuning)?

Thank you

Have you found any code for this?

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