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

2ed7d6b

1 Parent(s): bfc73d2

Update README.md

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@@ -33,10 +33,9 @@ pipeline_tag: text-generation
 ## Training Parameters
-- **Output Directory**: ./results
 - **Number of Training Epochs**: 1
 - **Mixed-Precision Training (fp16/bf16)**: False
-- **Batch Size per GPU for Training**: 4
 - **Batch Size per GPU for Evaluation**: 4
 - **Gradient Accumulation Steps**: 1
 - **Gradient Checkpointing**: True

 ## Training Parameters
 - **Number of Training Epochs**: 1
 - **Mixed-Precision Training (fp16/bf16)**: False
+- **Batch Size per GPU for Training**: 32
 - **Batch Size per GPU for Evaluation**: 4
 - **Gradient Accumulation Steps**: 1
 - **Gradient Checkpointing**: True