Instructions to use mitkox/sqlcoder-7b-2-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitkox/sqlcoder-7b-2-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mitkox/sqlcoder-7b-2-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mitkox/sqlcoder-7b-2-2") model = AutoModelForCausalLM.from_pretrained("mitkox/sqlcoder-7b-2-2") - MLX
How to use mitkox/sqlcoder-7b-2-2 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mitkox/sqlcoder-7b-2-2") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- vLLM
How to use mitkox/sqlcoder-7b-2-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mitkox/sqlcoder-7b-2-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mitkox/sqlcoder-7b-2-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mitkox/sqlcoder-7b-2-2
- SGLang
How to use mitkox/sqlcoder-7b-2-2 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 "mitkox/sqlcoder-7b-2-2" \ --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": "mitkox/sqlcoder-7b-2-2", "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 "mitkox/sqlcoder-7b-2-2" \ --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": "mitkox/sqlcoder-7b-2-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use mitkox/sqlcoder-7b-2-2 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mitkox/sqlcoder-7b-2-2" --prompt "Once upon a time"
- Docker Model Runner
How to use mitkox/sqlcoder-7b-2-2 with Docker Model Runner:
docker model run hf.co/mitkox/sqlcoder-7b-2-2
File size: 1,901 Bytes
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"content": "β<EOT>",
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"β<EOT>",
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"β<EOT>"
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"eot_token": "β<EOT>",
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