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azeemazam
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Model Details
Model Provider
azeemazam
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
To use this adapter, you need to load the base model first and then apply the LoRA weights.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelmodel_id = "Qwen/Qwen2.5-0.5B-Instruct"adapter_id = "azeemazam/Qwen2.5-0.5B-SQL"tokenizer = AutoTokenizer.from_pretrained(adapter_id)base_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map='auto')model = PeftModel.from_pretrained(base_model, adapter_id)def generate_sql(schema, question):messages = [{"role": "user", "content": f"Generate SQL.\\n\\nDatabase Schema:\\n{schema}\\n\\nQuestion:\\n{question}"}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors='pt').to(model.device)outputs = model.generate(**inputs, max_new_tokens=150)return tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)schema = "CREATE TABLE employees (id INT, name TEXT, salary INT)"question = "Who earns more than 50000?"print(generate_sql(schema, question))