This Model is based on Llama-2 7B model provided by Meta. The Model accepts text and return SQL-query. This Model has been fine-tuned on "NousResearch/Llama-2-7b-hf". | |
```python | |
# Use a pipeline as a high-level helper | |
from transformers import pipeline | |
pipe = pipeline("text2text-generation", model="ekshat/Llama-2-7b-chat-finetune-for-text2sql") | |
# Load model directly | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql") | |
model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql") | |
# Run text generation pipeline with our next model | |
context = "CREATE TABLE Student (name VARCHAR, college VARCHAR, age VARCHAR, group VARCHAR, marks VARCHAR)" | |
question = "List the name of Students belongs to school 'St. Xavier' and having marks greater than '600'" | |
prompt = f"""Below is an context that describes a sql query, paired with an question that provides further information. Write an answer that appropriately completes the request. | |
### Context: | |
{context} | |
### Question: | |
{question} | |
### Answer:""" | |
sequences = pipeline( | |
prompt, | |
do_sample=True, | |
top_k=10, | |
num_return_sequences=1, | |
eos_token_id=tokenizer.eos_token_id, | |
max_length=200, | |
) | |
for seq in sequences: | |
print(f"Result: {seq['generated_text']}") | |
``` |