Instructions to use MaXoN654/RUSQL-0.8B-Text2SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaXoN654/RUSQL-0.8B-Text2SQL with Transformers:

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

pipe = pipeline("text-generation", model="MaXoN654/RUSQL-0.8B-Text2SQL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaXoN654/RUSQL-0.8B-Text2SQL")
model = AutoModelForCausalLM.from_pretrained("MaXoN654/RUSQL-0.8B-Text2SQL", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaXoN654/RUSQL-0.8B-Text2SQL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaXoN654/RUSQL-0.8B-Text2SQL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaXoN654/RUSQL-0.8B-Text2SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaXoN654/RUSQL-0.8B-Text2SQL

SGLang

How to use MaXoN654/RUSQL-0.8B-Text2SQL 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 "MaXoN654/RUSQL-0.8B-Text2SQL" \
    --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": "MaXoN654/RUSQL-0.8B-Text2SQL",
		"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 "MaXoN654/RUSQL-0.8B-Text2SQL" \
        --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": "MaXoN654/RUSQL-0.8B-Text2SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaXoN654/RUSQL-0.8B-Text2SQL with Docker Model Runner:
```
docker model run hf.co/MaXoN654/RUSQL-0.8B-Text2SQL
```

MaXoN654 commited on Jul 19

Commit

efe1a62

verified ·

1 Parent(s): 24d05ff

model card: add base model per-complexity EX accuracy

Browse files

Files changed (1) hide show

README.md +8 -8

README.md CHANGED Viewed

@@ -42,14 +42,14 @@ are the same items, English question vs its translation), greedy decoding:
 Fine-tuning lifts execution accuracy **from 13.9% to 58.4%** — about 4.2× the base
 model, and above its English-question ceiling (16.0%).
-Breakdown by SQL complexity (RUSQL, RU questions):
-| Complexity | n | EX accuracy |
-|---|---|---|
-| Simple | 259 | 75.7% |
-| Moderate | 858 | 70.3% |
-| Complex | 973 | 55.1% |
-| Highly Complex | 639 | 40.5% |
 *All three rows are scored on the exact same 2,729 items (the QE-filtered held-out
 split), so the numbers are directly comparable. Greedy decoding. The base model also

 Fine-tuning lifts execution accuracy **from 13.9% to 58.4%** — about 4.2× the base
 model, and above its English-question ceiling (16.0%).
+Breakdown by SQL complexity (RU questions):
+| Complexity | n | Base EX | RUSQL EX |
+|---|---|---|---|
+| Simple | 259 | 28.6% | **75.7%** |
+| Moderate | 858 | 14.7% | **70.3%** |
+| Complex | 973 | 12.3% | **55.1%** |
+| Highly Complex | 639 | 9.2% | **40.5%** |
 *All three rows are scored on the exact same 2,729 items (the QE-filtered held-out
 split), so the numbers are directly comparable. Greedy decoding. The base model also