Instructions to use adamabuhamdan/tinyllama-sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adamabuhamdan/tinyllama-sql-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "adamabuhamdan/tinyllama-sql-lora")

Transformers

How to use adamabuhamdan/tinyllama-sql-lora with Transformers:

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

pipe = pipeline("text-generation", model="adamabuhamdan/tinyllama-sql-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("adamabuhamdan/tinyllama-sql-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use adamabuhamdan/tinyllama-sql-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/adamabuhamdan/tinyllama-sql-lora

SGLang

How to use adamabuhamdan/tinyllama-sql-lora 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 "adamabuhamdan/tinyllama-sql-lora" \
    --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": "adamabuhamdan/tinyllama-sql-lora",
		"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 "adamabuhamdan/tinyllama-sql-lora" \
        --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": "adamabuhamdan/tinyllama-sql-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use adamabuhamdan/tinyllama-sql-lora with Docker Model Runner:
```
docker model run hf.co/adamabuhamdan/tinyllama-sql-lora
```

tinyllama-sql-lora / README.md

adamabuhamdan

Update README.md

ce1c444 verified 4 days ago

preview code

raw

history blame contribute delete

2.34 kB

metadata

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
pipeline_tag: text-generation
tags:
  - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
  - lora
  - sft
  - transformers
  - trl
  - text-to-sql
datasets:
  - b-mc2/sql-create-context

🤖 TinyLlama Text-to-SQL (LoRA Adapter)

هذا النموذج عبارة عن LoRA Adapter تم تدريبه لتعديل سلوك نموذج TinyLlama-1.1B ليصبح متخصصاً في تحويل الأسئلة باللغة الطبيعية إلى كود SQL دقيق بناءً على هيكل الجدول (Schema) المعطى له، دون أي ثرثرة زائدة.

Developed by: Adam Abu Hamdan
Model type: PEFT (LoRA)
Language: English (Text & SQL)
Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

💡 كيف يعمل النموذج (How to Get Started)

يمكنك تشغيل هذا النموذج ودمج الأدابتر (الـ 20 ميجابايت) مع النموذج الأساسي باستخدام الكود التالي في بايثون:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
ADAPTER_MODEL = "adamabuhamdan/tinyllama-sql-lora"

# 1. تحميل المترجم والنموذج الأساسي
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    device_map="auto",
)

# 2. دمج أوزان LoRA التي قمنا بتدريبها
model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL)
model.eval()

# 3. تجربة توليد كود SQL
schema = "CREATE TABLE employees (id INT, name TEXT, department TEXT, salary INT);"
question = "List the names of employees in Engineering earning more than 100000."

prompt = f"<|system|>\nYou are a SQL assistant. Given a table schema and a question, reply with ONLY the SQL query, nothing else.</s>\n<|user|>\nSchema:\n{schema}\n\nQuestion: {question}</s>\n<|assistant|>\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=False)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip())