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lakshitha722
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Model Details
Model Provider
lakshitha722
Model Tree
unsloth/Llama-3.2-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Use the code below to load the model and generate SQL queries using Unsloth (recommended for local GPUs) or standard HuggingFace Transformers.
python
from unsloth import FastLanguageModelimport torchMODEL_NAME = "lakshitha722/querymind-nl2sql"model, tokenizer = FastLanguageModel.from_pretrained(model_name = MODEL_NAME,max_seq_length = 1024,load_in_4bit = True,dtype = None,)FastLanguageModel.for_inference(model)# 1. Define Prompt TemplatePROMPT_TEMPLATE = """Below is an instruction that describes a task. Write a response that appropriately completes the request.### Instruction:Convert the following natural language question to a SQL query based on the given database schema. Return ONLY the SQL query, nothing else.### Schema:{schema}### Question:{question}### Response:"""# 2. Prepare Inputsschema = "Database: company\nTables: employees (id, name, department, salary, hire_date)"question = "What is the average salary by department?"prompt = PROMPT_TEMPLATE.format(schema=schema, question=question)inputs = tokenizer([prompt], return_tensors="pt").to("cuda")# 3. Generatewith torch.no_grad():outputs = model.generate(**inputs,max_new_tokens = 150,temperature = 0.1,do_sample = False,pad_token_id = tokenizer.eos_token_id,)# 4. Decode Outputinput_length = inputs['input_ids'].shape[1]sql = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True).strip()print("Generated SQL:", sql)