LLMS.py

from llama_cpp import Llama
def die(type):
    llm = Llama(model_path="./DeepSeek-Coder-V2-Lite-Instruct-Q5_K_S.gguf")
    responses = llm(
    f"""I am a {type} person 
   Create a diet plan to lose weight for a month i Want in the format :
    Week 1:\n
      - Breakfast:\n
      - Lunch:\n
      - Dinner:\n
    Week 2:\n
      - Breakfast:\n
      - Lunch:\n
      - Dinner:\n
    Week 3:\n
      - Breakfast:\n
      - Lunch:\n
      - Dinner:\n
    Week 4:
      - Breakfast:\n
      - Lunch:\n
      - Dinner:\n
    """,
        max_tokens=1000,
        temperature=0.7,
        top_p=0.95,
        top_k=40
    )
    return responses['choices'][0]['text']
def di(type):
    llm = Llama(model_path="./DeepSeek-Coder-V2-Lite-Instruct-Q5_K_S.gguf")
    responses = llm(
        f""" I am a person
        Create a diet plan to {di} for a month i Want in the format :
        Week 1:\n
          - Breakfast:\n
          - Lunch:\n
          - Dinner:\n
        Week 2:\n
          - Breakfast:\n
          - Lunch:\n
          - Dinner:\n
        Week 3:\n
          - Breakfast:\n
          - Lunch:\n
          - Dinner:\n
        Week 4:
          - Breakfast:\n
          - Lunch:\n
          - Dinner:\n
        """,
        max_tokens=600,
        temperature=0.7,
        top_p=0.95,
        top_k=40
    )
    return responses['choices'][0]['text']

def chats(type):
    llm = Llama(model_path="./metamath-13b-v10Q4_K_M.gguf")
    responses = llm(
        type,
        max_tokens=100,  # Increased token limit to ensure full plan generation
        temperature=0.6,  # Lower temperature for more deterministic output
        top_p=0.8,  # Adjusted for balance between speed and quality
        top_k=50
    )
    return responses['choices'][0]['text']