Instructions to use AlioLeuchtmann/flash-sqlite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlioLeuchtmann/flash-sqlite with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AlioLeuchtmann/flash-sqlite")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AlioLeuchtmann/flash-sqlite")
model = AutoModelForMultimodalLM.from_pretrained("AlioLeuchtmann/flash-sqlite", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlioLeuchtmann/flash-sqlite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlioLeuchtmann/flash-sqlite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlioLeuchtmann/flash-sqlite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlioLeuchtmann/flash-sqlite

SGLang

How to use AlioLeuchtmann/flash-sqlite 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 "AlioLeuchtmann/flash-sqlite" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlioLeuchtmann/flash-sqlite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "AlioLeuchtmann/flash-sqlite" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlioLeuchtmann/flash-sqlite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlioLeuchtmann/flash-sqlite with Docker Model Runner:
```
docker model run hf.co/AlioLeuchtmann/flash-sqlite
```

AlioLeuchtmann commited on 4 days ago

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@@ -61,11 +61,11 @@ Both roles use the same checkpoint. There is no auxiliary model, no embedding mo
 The BIRD team scored the model on the hidden test set: 32 candidates per question plus self-judge, on a single A100 80 GB.
-| metric | simple | moderate | challenging | **total** |
 |---|---|---|---|---|
 | Execution Accuracy (EX) | 80.19 | 71.53 | 56.49 | **73.73** |
-| Soft F1 | | | | **74.63** |
-| R-VES | | | | **67.17** |
 ### How it compares

 The BIRD team scored the model on the hidden test set: 32 candidates per question plus self-judge, on a single A100 80 GB.
+| metric | simple (949) | moderate (555) | challenging (285) | **total (1,789)** |
 |---|---|---|---|---|
 | Execution Accuracy (EX) | 80.19 | 71.53 | 56.49 | **73.73** |
+| Soft F1 | 80.50 | 72.88 | 58.49 | **74.63** |
+| R-VES | 73.54 | 64.29 | 51.55 | **67.17** |
 ### How it compares