Instructions to use bugdaryan/Code-Llama-2-13B-instruct-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bugdaryan/Code-Llama-2-13B-instruct-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="bugdaryan/Code-Llama-2-13B-instruct-text2sql")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bugdaryan/Code-Llama-2-13B-instruct-text2sql")
model = AutoModelForCausalLM.from_pretrained("bugdaryan/Code-Llama-2-13B-instruct-text2sql", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bugdaryan/Code-Llama-2-13B-instruct-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bugdaryan/Code-Llama-2-13B-instruct-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bugdaryan/Code-Llama-2-13B-instruct-text2sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bugdaryan/Code-Llama-2-13B-instruct-text2sql

SGLang

How to use bugdaryan/Code-Llama-2-13B-instruct-text2sql 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 "bugdaryan/Code-Llama-2-13B-instruct-text2sql" \
    --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": "bugdaryan/Code-Llama-2-13B-instruct-text2sql",
		"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 "bugdaryan/Code-Llama-2-13B-instruct-text2sql" \
        --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": "bugdaryan/Code-Llama-2-13B-instruct-text2sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bugdaryan/Code-Llama-2-13B-instruct-text2sql with Docker Model Runner:
```
docker model run hf.co/bugdaryan/Code-Llama-2-13B-instruct-text2sql
```

bugdaryan commited on Sep 7, 2023

Commit

8725cd2

1 Parent(s): 571ba32

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@@ -140,7 +140,6 @@ Use the code below to get started with the model.
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]

 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]