Update README.md
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README.md
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### Testing results
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import torch
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from peft import AutoPeftModelForCausalLM
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from transformers import AutoTokenizer, pipeline
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peft_model_id = "frankmorales2020/Mistral-7B-text-to-sql-without-flash-attention-2"
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tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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prompt='What was the first album Beyoncé released as a solo artist?'
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prompt = f"Instruct: generate a SQL query.\n{prompt}\nOutput:\n" # for dataset b-mc2/sql-create-context
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outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)
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### Testing results
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import torch
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from peft import AutoPeftModelForCausalLM
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from transformers import AutoTokenizer, pipeline
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peft_model_id = "frankmorales2020/Mistral-7B-text-to-sql-without-flash-attention-2"
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tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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CASE Number 1:
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prompt='What was the first album Beyoncé released as a solo artist?'
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prompt = f"Instruct: generate a SQL query.\n{prompt}\nOutput:\n" # for dataset b-mc2/sql-create-context
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outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)
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