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README.md
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train/global_step : 10
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### Framework versions
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train/global_step : 10
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## Inference Code
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After doing necessary imports
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device_map = {"": 0}
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model_id = "mistralai/Mistral-7B-v0.1"
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new_model = "Akil15/mistral_SQL_v.0.1"
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# Reload model in FP16 and merge it with LoRA weights
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base_model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map=device_map,
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)
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model = PeftModel.from_pretrained(base_model, new_model)
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model = model.merge_and_unload()
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# Reload tokenizer to save it
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "right"
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sample text(example):
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text = """Question: How many vegetable farms with over 100 acres of cultivated land utilize organic farming methods, and what is the average yield per acre for these farms?
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Context:CREATE TABLE vegetable_farm (Acres INTEGER,Organic BOOLEAN,Yield_Per_Acre DECIMAL);"""
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text = input()
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inputs = tokenizer(text, return_tensors="pt").to(device)
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outputs = model.generate(**inputs, max_new_tokens=20)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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Note: Change the max_new_tokens length based on the question-context text input or just define it to 100
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### Framework versions
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