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--- |
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library_name: transformers |
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tags: |
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- code |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# Model Card for Model ID |
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This model is trained on generating SQL code from user prompts. |
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## Model Details |
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### Model Description |
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This model is traiend for generating SQL code from user prompts. The prompt structure is based on this format. |
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###Question ###Context[SQL code of your table ] ###Answer: |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Ali Bidaran |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** Gemma 2B |
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### Model Sources [optional] |
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### Direct Use |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer |
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model_id = "Gemma2_SQLGEN" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0}) |
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tokenizer.padding_side = 'right' |
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) |
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from peft import LoraConfig, PeftModel, get_peft_model |
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from trl import SFTTrainer |
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prompt = "find unique items from name coloum." |
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text=f"<s>##Question: {prompt} \n ##Context: CREATE TABLE head (head_id VARCHAR, name VARCHAR) \n ##Answer:" |
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inputs=tokenizer(text,return_tensors='pt').to('cuda') |
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outputs=model.generate(**inputs,max_new_tokens=400,do_sample=True,top_p=0.92,top_k=10,temperature=0.7) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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