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
```

Quantize version

#10

by baconnier - opened Sep 8, 2023

Discussion

baconnier

Sep 8, 2023

Hi di you plan to make a quantized version or the result will not be accurate ?

senwu

NumbersStation org Sep 9, 2023

That's on our todo list. Please stay tuned! :)

IshaanShettigar

Sep 19, 2023

Currently we can't run it on a free tier colab notebook. It gives me RAM limit exceeded. So we'd love a quantized version.
Any idea if this works on the pro tier? and what might be the RAM and VRAM requirements to run this?

AayushShah

Oct 30, 2023

Any possibility of this model in the GGUF format? Like available in the Bloke's repositories?
Or... will I need to convert it manually?

Please share any link if you guys find one.
Alternatively I am opening a separate discussion on this project for the same.

🍻

flymonk

Oct 30, 2023

You can use convert tool from llama.cpp to build gguf format.

AayushShah

Oct 30, 2023

Yeah of course, thanks

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