Instructions to use llmware/slim-sql-1b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-sql-1b-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmware/slim-sql-1b-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sql-1b-v0") model = AutoModelForCausalLM.from_pretrained("llmware/slim-sql-1b-v0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use llmware/slim-sql-1b-v0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmware/slim-sql-1b-v0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmware/slim-sql-1b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llmware/slim-sql-1b-v0
- SGLang
How to use llmware/slim-sql-1b-v0 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 "llmware/slim-sql-1b-v0" \ --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": "llmware/slim-sql-1b-v0", "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 "llmware/slim-sql-1b-v0" \ --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": "llmware/slim-sql-1b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use llmware/slim-sql-1b-v0 with Docker Model Runner:
docker model run hf.co/llmware/slim-sql-1b-v0
Commit ·
ea36597
1
Parent(s): c38d876
Update README.md
Browse files
README.md
CHANGED
|
@@ -31,7 +31,7 @@ Evaluated against 100 test SQL queries with under 100 characters. 1 point given
|
|
| 31 |
|
| 32 |
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
| 33 |
|
| 34 |
-
slim-sql-1b-v0 is designed to generate accurate SQL queries for data
|
| 35 |
For best results, prompts should be structured as a question to retrieve information and perform aggregate functions on one or several variables.
|
| 36 |
|
| 37 |
## Bias, Risks, and Limitations
|
|
|
|
| 31 |
|
| 32 |
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
| 33 |
|
| 34 |
+
slim-sql-1b-v0 is designed to generate accurate SQL queries for data retrieval on simple table structures given a natural language prompt.
|
| 35 |
For best results, prompts should be structured as a question to retrieve information and perform aggregate functions on one or several variables.
|
| 36 |
|
| 37 |
## Bias, Risks, and Limitations
|