Instructions to use rakeshkiriyath/gpt2Medium_text_to_sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rakeshkiriyath/gpt2Medium_text_to_sql with Transformers:

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

pipe = pipeline("text-generation", model="rakeshkiriyath/gpt2Medium_text_to_sql")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql")
model = AutoModelForCausalLM.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rakeshkiriyath/gpt2Medium_text_to_sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rakeshkiriyath/gpt2Medium_text_to_sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rakeshkiriyath/gpt2Medium_text_to_sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rakeshkiriyath/gpt2Medium_text_to_sql

SGLang

How to use rakeshkiriyath/gpt2Medium_text_to_sql 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 "rakeshkiriyath/gpt2Medium_text_to_sql" \
    --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": "rakeshkiriyath/gpt2Medium_text_to_sql",
		"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 "rakeshkiriyath/gpt2Medium_text_to_sql" \
        --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": "rakeshkiriyath/gpt2Medium_text_to_sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rakeshkiriyath/gpt2Medium_text_to_sql with Docker Model Runner:
```
docker model run hf.co/rakeshkiriyath/gpt2Medium_text_to_sql
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card

This is my first fine tuned LLM project.

Usage

from transformers import GPT2LMHeadModel, GPT2Tokenizer

finetunedGPT = GPT2LMHeadModel.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql")
finetunedTokenizer = GPT2Tokenizer.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql")

def generate_text_to_sql(query, model, tokenizer, max_length=256):
    prompt = f"Translate the following English question to SQL: {query}"

    input_tensor = tokenizer.encode(prompt, return_tensors='pt').to('cuda')

    output = model.generate(input_tensor, max_length=max_length, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)

    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

    # Return only the SQL part (removing the input text)
    sql_output = decoded_output[len(prompt):].strip()

    return sql_output

queryList = ["I need a list of employees who joined in the company last 6 months with a salary hike of 30% ",
             "Give me loginid,status,company of a user who is mapped to the organization XYZ "]

for query in queryList:

  sql_result = generate_text_to_sql(query, finetunedGPT, finetunedTokenizer)
  print(sql_result,"\n")

Output

SELECT COUNT(*) FROM employees WHERE last_6_months = "6 months" AND salary_hike = "30%"
SELECT loginid,status,company FROM user_mapped_to_organization WHERE mapping = "XYZ"

Training Hyperparameters

num_train_epochs=1
per_device_train_batch_size=3
gradient_accumulation_steps=9
learning_rate=5e-5
weight_decay=0.01

Evaluation

Step	Training Loss
500	0.337800
1000	0.262900
1500	0.253200
2000	0.246400

{'eval_loss': 0.23689331114292145, 'eval_runtime': 104.4102, 'eval_samples_per_second': 67.043, 'eval_steps_per_second': 8.38, 'epoch': 1.0}

Downloads last month: 11

Model tree for rakeshkiriyath/gpt2Medium_text_to_sql

Quantizations

1 model

rakeshkiriyath
/

gpt2Medium_text_to_sql