Instructions to use InfometryINC/phi15-retail-sql-lora-jetson-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InfometryINC/phi15-retail-sql-lora-jetson-v2 with Transformers:

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

pipe = pipeline("text-generation", model="InfometryINC/phi15-retail-sql-lora-jetson-v2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("InfometryINC/phi15-retail-sql-lora-jetson-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InfometryINC/phi15-retail-sql-lora-jetson-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InfometryINC/phi15-retail-sql-lora-jetson-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InfometryINC/phi15-retail-sql-lora-jetson-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/InfometryINC/phi15-retail-sql-lora-jetson-v2

SGLang

How to use InfometryINC/phi15-retail-sql-lora-jetson-v2 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 "InfometryINC/phi15-retail-sql-lora-jetson-v2" \
    --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": "InfometryINC/phi15-retail-sql-lora-jetson-v2",
		"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 "InfometryINC/phi15-retail-sql-lora-jetson-v2" \
        --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": "InfometryINC/phi15-retail-sql-lora-jetson-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use InfometryINC/phi15-retail-sql-lora-jetson-v2 with Docker Model Runner:
```
docker model run hf.co/InfometryINC/phi15-retail-sql-lora-jetson-v2
```

phi15-retail-sql-lora-jetson-v2

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---
tags: [text-to-sql, tpc-ds, qlora, phi-1_5, retail, jetson, 4bit]

pipeline_tag: text-generation
library_name: transformers
---
# Phi-1.5 TPC-DS SQL QLoRA — v2 (Jetson)
- Base model  : microsoft/phi-1_5
- Method      : QLoRA (4-bit NF4 + fp16 compute, bitsandbytes 0.48.0.dev0)
- Device      : NVIDIA Jetson Orin Nano 8GB / JetPack 6.2
- Samples     : 1000 | Epochs: 5 | LR: 0.0002
- BLEU        : 0.31% | Exact Match: 0.0%
- Trained     : 2026-06-09T21:06:58.536747

## Load this adapter
```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                          bnb_4bit_compute_dtype=torch.float16)
base = AutoModelForCausalLM.from_pretrained(
    "microsoft/phi-1_5", quantization_config=bnb,
    device_map={"": 0}, trust_remote_code=True)
model = PeftModel.from_pretrained(base, "InfometryINC/phi15-retail-sql-lora-jetson-v2")
```