GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
lakshitha722
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
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 torch MODEL_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)from unsloth import FastLanguageModelimport torch MODEL_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)