Instructions to use XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy with Transformers:

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

pipe = pipeline("text-generation", model="XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy")
model = AutoModelForCausalLM.from_pretrained("XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy")
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

vLLM

How to use XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy

SGLang

How to use XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy 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 "XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy" \
    --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": "XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy",
		"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 "XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy" \
        --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": "XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy with Docker Model Runner:
```
docker model run hf.co/XeAI/LLaMa_3.2_3B_Instruct_Text2SQL_Legacy
```

ZhafranR commited on Nov 7, 2024

Commit

b00b0fc

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1 Parent(s): b837da5

Update README.md

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@@ -14,7 +14,7 @@ pipeline_tag: text-generation
 This is a fine-tuned version of LLaMA 3.2 3B Instruct model, specifically optimized for Text-to-SQL generation tasks. The model has been trained to convert natural language queries into structured SQL commands.
-- **Developed by:** Zhafran Ramadhan
 - **Model type:** Decoder-only Language Model
 - **Language(s):** English - MultiLingual
 - **License:** MIT
@@ -52,7 +52,7 @@ from transformers import pipeline
 # Initialize the pipeline
 generator = pipeline(
     "text-generation",
-    model="[YOUR_HUGGINGFACE_MODEL_ID]",  # Replace with your model ID
     torch_dtype=torch.float16,
     device_map="auto"
 )

 This is a fine-tuned version of LLaMA 3.2 3B Instruct model, specifically optimized for Text-to-SQL generation tasks. The model has been trained to convert natural language queries into structured SQL commands.
+- **Developed by:** Zhafran Ramadhan - XeAI
 - **Model type:** Decoder-only Language Model
 - **Language(s):** English - MultiLingual
 - **License:** MIT
 # Initialize the pipeline
 generator = pipeline(
     "text-generation",
+    model="XeAI/LLaMa_3.2_3B_Instruct_Text2SQL",  # Replace with your model ID
     torch_dtype=torch.float16,
     device_map="auto"
 )