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

Libraries

How to use defog/sqlcoder-7b-2 with Transformers:

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

pipe = pipeline("text-generation", model="defog/sqlcoder-7b-2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("defog/sqlcoder-7b-2")
model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-7b-2")

llama-cpp-python

How to use defog/sqlcoder-7b-2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="defog/sqlcoder-7b-2",
	filename="sqlcoder-7b-q5_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use defog/sqlcoder-7b-2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf defog/sqlcoder-7b-2:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf defog/sqlcoder-7b-2:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf defog/sqlcoder-7b-2:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf defog/sqlcoder-7b-2:Q5_K_M

Use Docker

docker model run hf.co/defog/sqlcoder-7b-2:Q5_K_M

LM Studio
Jan

vLLM

How to use defog/sqlcoder-7b-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "defog/sqlcoder-7b-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "defog/sqlcoder-7b-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/defog/sqlcoder-7b-2:Q5_K_M

SGLang

How to use defog/sqlcoder-7b-2 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 "defog/sqlcoder-7b-2" \
    --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": "defog/sqlcoder-7b-2",
		"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 "defog/sqlcoder-7b-2" \
        --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": "defog/sqlcoder-7b-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use defog/sqlcoder-7b-2 with Ollama:
```
ollama run hf.co/defog/sqlcoder-7b-2:Q5_K_M
```

Unsloth Studio new

How to use defog/sqlcoder-7b-2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for defog/sqlcoder-7b-2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for defog/sqlcoder-7b-2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for defog/sqlcoder-7b-2 to start chatting

Docker Model Runner
How to use defog/sqlcoder-7b-2 with Docker Model Runner:
```
docker model run hf.co/defog/sqlcoder-7b-2:Q5_K_M
```

Lemonade

How to use defog/sqlcoder-7b-2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull defog/sqlcoder-7b-2:Q5_K_M

Run and chat with the model

lemonade run user.sqlcoder-7b-2-Q5_K_M

List all available models

lemonade list

Fine-tuning with SQL coder

#14

by Iamexperimenting - opened Feb 27, 2024

Discussion

Iamexperimenting

Feb 27, 2024

Hi @rishdotblog , since I have 370 tables in my database and as I know I won't be able to pass the entire 370 schema and question as prompt to the model to get the sql query.

So, I thought I can do domain based fine-tuning with SQLCoder model with my all my 370 schema and later I can convert it that as instruction finetuning. Because I assume after I fine tune the model with my 370 schema , the will have information about my database schema, so I can ask questions directly to the model and get SQL query as an output.

is it doable? or is my understanding is correct?

srivatsan32

Feb 28, 2024

@Iamexperimenting any progress with the above?

rishdotblog

Defog.ai org Feb 28, 2024

Yup, that's correct @Iamexperimenting .

rishdotblog changed discussion status to closed Feb 28, 2024

Iamexperimenting

Feb 29, 2024

•

edited Feb 29, 2024

@rishdotblog @wongjingping but I have a little confusion, I assume SQLCoder base model would have been trained with other internet database schema. In that case, model would hallucinate right?

Also, have you tried the approach which mentioned above? from domain based finetuning to chat based fine tuning.

Iamexperimenting

Mar 10, 2024

@rishdotblog could you please share your thoughts?

wongjingping

Mar 11, 2024

@Iamexperimenting we always train with the schema of interest inside the prompt, as it helps the model to learn that it needs to actually see the required column names and not hallucinate them. We have yet to try chat-based finetuning, please let us know how your results turn out if you do!

mustafaAPD

Dec 11, 2024

@rishdotblog @wongjingping but I have a little confusion, I assume SQLCoder base model would have been trained with other internet database schema. In that case, model would hallucinate right?

Also, have you tried the approach which mentioned above? from domain based finetuning to chat based fine tuning.

Hi, have you tried fine-tuning? I am thinking of doing the same and help from you would be very great.

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