Instructions to use defog/sqlcoder-7b-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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 This model for my dataset consisting of question and SQL.
For the training purpose i am using this prompt:
input_prompt = f"""Task Generate a SQL query to answer the question using the given Tenant ID.
Tenant ID: {tenant}
[QUESTION]{q}[/QUESTION]
SQL Query
[SQL][/SQL]"""
label = f"""Task Generate a SQL query to answer the question using the given Tenant ID.
Tenant ID: {tenant}
[QUESTION]{q}[/QUESTION]
SQL Query
[SQL]{sql}[/SQL]"""
inputs.append(input_prompt)
labels.append(label)
# Tokenize the inputs
model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
model_labels = tokenizer(labels, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
model_inputs["labels"] = model_labels["input_ids"]
I am using these inputs prompt and label and then using their tokens to train my model but its not getting me any accuracy.
Hi Pratik
I also got a similar task where I need to train a text-SQL model on my organization's dataset and pull an SQL query to run. Could you help me with the dataset and the prompt you have used to train this model?
Any help will be appreciated