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  LoRA +Finetuned with 50 pairs of GPT-3.5-turbo generated workout QA pair on T4 Google Collab using GPT-neo-1.3B base model.
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  Finetuned on synthetic data.
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- Collab Link https://colab.research.google.com/drive/12uv_PocrcDmvOhjPD9SGAqcXbpZAZh2a?authuser=2#scrollTo=PNcc7C51VWHV
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  LoRA +Finetuned with 50 pairs of GPT-3.5-turbo generated workout QA pair on T4 Google Collab using GPT-neo-1.3B base model.
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  Finetuned on synthetic data.
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+ Collab Link https://colab.research.google.com/drive/12uv_PocrcDmvOhjPD9SGAqcXbpZAZh2a?authuser=2#scrollTo=PNcc7C51VWHV
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+
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+ # How to use the model
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("Prajna1999/Prajna-gpt-neo-1.3B-fitbot")
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+ model = AutoModelForCausalLM.from_pretrained("Prajna1999/Prajna-gpt-neo-1.3B-fitbot")
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+
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+ input_ids = tokenizer.encode("Suggest some workouts for weight loss", return_tensors="pt")
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+ output = model.generate(input_ids, max_length=128,
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+ temperature=0, top_p=0.7, top_k=2)
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+ output_text = tokenizer.decode(output[0], skip_special_tokens=True)
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+
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+ print(output_text)