Instructions to use ai2sql/ai2sql_llama-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ai2sql/ai2sql_llama-2-7b with Transformers:

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

pipe = pipeline("text-generation", model="ai2sql/ai2sql_llama-2-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ai2sql/ai2sql_llama-2-7b")
model = AutoModelForCausalLM.from_pretrained("ai2sql/ai2sql_llama-2-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ai2sql/ai2sql_llama-2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ai2sql/ai2sql_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": "ai2sql/ai2sql_llama-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ai2sql/ai2sql_llama-2-7b

SGLang

How to use ai2sql/ai2sql_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 "ai2sql/ai2sql_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": "ai2sql/ai2sql_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 "ai2sql/ai2sql_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": "ai2sql/ai2sql_llama-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ai2sql/ai2sql_llama-2-7b with Docker Model Runner:
```
docker model run hf.co/ai2sql/ai2sql_llama-2-7b
```

ai2sql_llama-2-7b / README.md

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	---
	datasets:
	- wikisql
	pipeline_tag: text-generation
	tags:
	- llama
	---

	# AI2sql

	AI2sql is a state-of-the-art LLM for converting natural language questions to SQL queries.

	# Model Card: Fine-tuning Llama 2 for AI2SQL Query Generation

	This model card outlines the fine-tuning of the Llama 2 model to generate SQL queries for AI2SQL tasks.

	## Model Details
	- Original Model: NousResearch/Llama-2-7b-chat-hf
	- Model Type: Large Language Model
	- Fine-tuning Task: AI2SQL (SQL Query Generation)
	- Fine-tuned Model Name: llama-2-7b-miniguanaco

	## Implementation
	- Environment Requirement: GPU-supported platform with minimum 20GB RAM.
	- Dependencies: accelerate==0.21.0, peft==0.4.0, bitsandbytes==0.40.2, transformers==4.31.0, trl==0.4.7
	- GPU Specification: T4 or equivalent (as of 24 Aug 2023)

	## Training Details
	- Dataset: WikiSQL
	- Method: Supervised Fine-Tuning (SFT)
	- Epochs: 1
	- Batch Size: 4 per GPU
	- Optimization: AdamW with cosine learning rate schedule
	- Learning Rate: 2e-4
	- Special Features:
	- LoRA for efficient parameter adjustment.
	- 4-bit precision model loading with BitsAndBytes.
	- Gradient checkpointing and clipping.

	## Performance Metrics
	- Accuracy: 85% (on a held-out test set from WikiSQL)
	- Query Generation Time: Average of 0.5 seconds per query
	- Resource Efficiency: Demonstrates 30% reduced memory usage compared to the base model

	## Usage and Applications
	TBD


	Note: The performance metrics provided here are hypothetical and for illustrative purposes only. Actual performance would depend on various factors, including the specifics of the dataset and training regimen.