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")

# 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")

Notebooks
Google Colab
Kaggle
Local Apps

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

4ecd1fc

1 Parent(s): 2ed7d6b

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@@ -14,8 +14,8 @@ pipeline_tag: text-generation
 ## Model Information
-- **Base Model**: [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf)
-- **Finetuning Dataset**: [bugdaryan/sql-create-context-instruction](Dataset URL)
 - **Training Time**: Approximately 4 hours on 2 V100 32GB GPUs
 ## LoRA Parameters

 ## Model Information
+- **Base Model**: [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf)
+- **Finetuning Dataset**: [bugdaryan/sql-create-context-instruction](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction)
 - **Training Time**: Approximately 4 hours on 2 V100 32GB GPUs
 ## LoRA Parameters