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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use defog/sqlcoder-7b-2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
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
- Atomic Chat new
- 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
metadata
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: sqlcoder_7b_fullft_ds7_linear
results: []
sqlcoder_7b_fullft_ds7_linear
This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3517
- Sql Exact Match String: 0
- Tokens Match Avg: 0.9014
- First Index Mismatch Avg: 2.2356
- Mean Mismatch I Diff Avg: 12.5313
- Count Mismatch I Diff Avg: 6.2756
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 600
Training results
| Training Loss | Epoch | Step | Validation Loss | Sql Exact Match String | Tokens Match Avg | First Index Mismatch Avg | Mean Mismatch I Diff Avg | Count Mismatch I Diff Avg |
|---|---|---|---|---|---|---|---|---|
| 0.14 | 0.1 | 100 | 0.3510 | 0 | 0.8940 | 2.0844 | 11.4371 | 6.88 |
| 0.1083 | 0.2 | 200 | 0.3677 | 0 | 0.8930 | 2.1733 | 11.3445 | 6.6044 |
| 0.0912 | 0.3 | 300 | 0.3710 | 0 | 0.8953 | 2.2444 | 12.0020 | 6.44 |
| 0.0699 | 0.4 | 400 | 0.3598 | 0 | 0.8996 | 2.1778 | 12.3582 | 6.3289 |
| 0.0619 | 0.5 | 500 | 0.3516 | 0 | 0.9010 | 2.2489 | 12.6065 | 6.2756 |
| 0.0766 | 0.6 | 600 | 0.3517 | 0 | 0.9014 | 2.2356 | 12.5313 | 6.2756 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1