Instructions to use Chat2DB/Chat2DB-SQL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chat2DB/Chat2DB-SQL-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chat2DB/Chat2DB-SQL-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chat2DB/Chat2DB-SQL-7B") model = AutoModelForCausalLM.from_pretrained("Chat2DB/Chat2DB-SQL-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Chat2DB/Chat2DB-SQL-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chat2DB/Chat2DB-SQL-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chat2DB/Chat2DB-SQL-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chat2DB/Chat2DB-SQL-7B
- SGLang
How to use Chat2DB/Chat2DB-SQL-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 "Chat2DB/Chat2DB-SQL-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": "Chat2DB/Chat2DB-SQL-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 "Chat2DB/Chat2DB-SQL-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": "Chat2DB/Chat2DB-SQL-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chat2DB/Chat2DB-SQL-7B with Docker Model Runner:
docker model run hf.co/Chat2DB/Chat2DB-SQL-7B
Download pytorch_model-00001-of-00002.bin from Chat2DB/Chat2DB-SQL-7B: direct link, hf CLI and curl.
- Browser
- Download file 9.98 GB
-
https://huggingface.co/Chat2DB/Chat2DB-SQL-7B/resolve/8aaae9968a91e79660fbb5c8d64aa0977cf1367d/pytorch_model-00001-of-00002.bin
- Command line
-
hf download hf://Chat2DB/Chat2DB-SQL-7B@8aaae9968a91e79660fbb5c8d64aa0977cf1367d/pytorch_model-00001-of-00002.bin
-
curl -L -o pytorch_model-00001-of-00002.bin https://huggingface.co/Chat2DB/Chat2DB-SQL-7B/resolve/8aaae9968a91e79660fbb5c8d64aa0977cf1367d/pytorch_model-00001-of-00002.bin
9.98 GB
- Xet hash:
- 27a37e16f9b3c90399cd9fad6860883ba7cec0ead1a846818d44ddf63046fd80
- Size of remote file:
- 9.98 GB
- SHA256:
- 30f3bd3b5d0dfc4dd1c3b4e79fccf01d49d0d7f51792b2813b11fe40db14143e
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