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RahulPi
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Model Details
Model Provider
RahulPi
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This model is a fine-tuned, merged version of Qwen/Qwen2.5-1.5B-Instruct. It was trained using QLoRA with Hugging Face's SFTTrainer on a structured SQL context dataset and subsequently merged back into full 16-bit precision (fp16) for direct inference.
Generating SQL queries based on database context and user prompts.
The model was trained using the following template format:
text
System: You are a strict SQL assistant. Output ONLY valid SQL queries.User: Schema: <context/schema> Question: <natural_language_question>Assistant: <sql_query>
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "RahulPi/qwen2.5-1.5B-sql"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.float16,device_map="auto")context = "CREATE TABLE head (born_state VARCHAR, age INTEGER)"question = "How many heads were born in California and are older than 50?"prompt = (f"System: You are a strict SQL assistant. Output ONLY valid SQL queries.\n"f"User: Schema: {context} Question: {question}\n"f"Assistant:")inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=128)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Trained on a 1,000-row subset of b-mc2/sql-create-context, combining table schemas and questions mapped directly to correct SQL target queries.
SFTTrainer with PEFT (QLoRA)NormalFloat4) during training, merged into fp16 for distributionq_proj, k_proj, v_proj, o_projpaged_adamw_32bit