Instructions to use Ary-007/Text-to-sql-llama-3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ary-007/Text-to-sql-llama-3.2 with Transformers:

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

pipe = pipeline("text-generation", model="Ary-007/Text-to-sql-llama-3.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ary-007/Text-to-sql-llama-3.2")
model = AutoModelForCausalLM.from_pretrained("Ary-007/Text-to-sql-llama-3.2", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ary-007/Text-to-sql-llama-3.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ary-007/Text-to-sql-llama-3.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ary-007/Text-to-sql-llama-3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ary-007/Text-to-sql-llama-3.2

SGLang

How to use Ary-007/Text-to-sql-llama-3.2 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 "Ary-007/Text-to-sql-llama-3.2" \
    --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": "Ary-007/Text-to-sql-llama-3.2",
		"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 "Ary-007/Text-to-sql-llama-3.2" \
        --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": "Ary-007/Text-to-sql-llama-3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ary-007/Text-to-sql-llama-3.2 with Docker Model Runner:
```
docker model run hf.co/Ary-007/Text-to-sql-llama-3.2
```

Ary-007 commited on Nov 28, 2025

Commit

012fa32

verified ·

1 Parent(s): 9e609dc

Update README.md

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README.md +53 -10

README.md CHANGED Viewed

@@ -19,9 +19,9 @@ This model is a fine-tuned version of Llama-3.2-3B-Instruct designed specificall
 It is lightweight (3B parameters), making it suitable for local deployment on consumer GPUs using 4-bit quantization.
 ### Model Description
-Base Model: unsloth/Llama-3.2-3B-Instruct
-Fine-tuning Framework: Unsloth (QLoRA)
-Dataset: gretelai/synthetic_text_to_sql
 ## Uses
@@ -32,11 +32,54 @@ The model was trained using the Alpaca prompt format. For best results, structur
 ![image](https://cdn-uploads.huggingface.co/production/uploads/656b8d33e8bf55919a6aa345/1XEAfrZ5iU7doBDy_T5ef.png)
 ## How to Get Started with the Model
-#### Summary
-]

 It is lightweight (3B parameters), making it suitable for local deployment on consumer GPUs using 4-bit quantization.
 ### Model Description
+1) Base Model: unsloth/Llama-3.2-3B-Instruct
+2) Fine-tuning Framework: Unsloth (QLoRA)
+3) Dataset: gretelai/synthetic_text_to_sql
 ## Uses
 ![image](https://cdn-uploads.huggingface.co/production/uploads/656b8d33e8bf55919a6aa345/1XEAfrZ5iU7doBDy_T5ef.png)
 ## How to Get Started with the Model
+'''python
+import torch
+from transformers import pipeline
+model_id = "Ary-007/Text-to-sql-llama-3.2"
+# Load the pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    device_map="auto",
+)
+# Define the schema (Context)
+schema = """
+CREATE TABLE employees (
+    id INT,
+    name TEXT,
+    department TEXT,
+    salary INT,
+    hire_date DATE
+);
+"""
+# Define the user question
+question = "Find the name and salary of employees in the 'Engineering' department who earn more than 80000."
+# Format the prompt exactly as trained
+prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+Company Database : {schema}
+### Input:
+SQL Prompt :{question}
+### Response:
+"""
+outputs = pipe(
+    prompt,
+    max_new_tokens=200,
+    do_sample=True,
+    temperature=0.1,
+    top_p=0.9
+)
+print(outputs[0]["generated_text"])
+'''