roberta-prompt_classifier-v0.1

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Intention of the model is to determine if the given user prompt's complexity, domain question requires a SOTA (very large) LLM or can be deescaleted to a smaller or local model.

Example code:

from openai import OpenAI
from datasets import load_dataset
from datasets.dataset_dict import DatasetDict
import json
import random
from transformers import (
    RobertaTokenizerFast,
    RobertaForSequenceClassification,
)
from transformers import pipeline

model_id = 'DevQuasar/roberta-prompt_classifier-v0.1'
tokenizer = RobertaTokenizerFast.from_pretrained(model_id)
sentence_classifier = pipeline(
    "sentiment-analysis", model=model_id, tokenizer=tokenizer
)

model_store = {
                "small_llm": {
                    "escalation_order": 0,
                    "url": "http://localhost:1234/v1",
                    "api_key": "lm-studio",
                    "model_id": "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf",
                    "max_ctx": 4096
                },
                "large_llm": {
                    "escalation_order": 1,
                    "url": "http://localhost:1234/v1",
                    "api_key": "lm-studio",
                    "model_id": "lmstudio-community/Meta-Llama-3-70B-Instruct-GGUF/Meta-Llama-3-70B-Instruct-Q4_K_M.gguf",
                    "max_ctx": 8192 
                }
}

def prompt_classifier(user_prompt):
    return sentence_classifier(user_prompt)[0]['label']

def llm_router(user_prompt, tokens_so_far = 0):
    return model_store[prompt_classifier(user_prompt)]

def chat(user_prompt, model_store_entry = None, curr_ctx = [], system_prompt = ' ', verbose=False):
    if model_store_entry == None and curr_ctx == []:
        # initial model selection
        model_store_entry = llm_router(user_prompt)
        if verbose:
            print(f'Classify prompt - selected model: {model_store_entry["model_id"]}')
    else:
        #handle escalation
        model_store_candidate = llm_router(user_prompt)
        if model_store_candidate["escalation_order"] >  model_store_entry["escalation_order"]:
            model_store_entry = model_store_candidate
            if verbose:
                print(f'Escalate model - selected model: {model_store_entry["model_id"]}')
    url = model_store_entry['url']
    api_key = model_store_entry['api_key']
    model_id = model_store_entry['model_id']
    
    client = OpenAI(base_url=url, api_key=api_key)
    messages = curr_ctx
    messages.append({"role": "user", "content": user_prompt})
    
    completion = client.chat.completions.create(
      model=model_id,
      messages = messages,
      temperature=0.7,
    )
    messages.append({"role": "assistant", "content": completion.choices[0].message.content})
    if verbose:
        print(f'Used model: {model_id}')
        print(f'completion: {completion}')
    client.close()
    return completion.choices[0].message.content, messages, model_store_entry

use_model = None
ctx = []
# start with simple prompt -> llama3-8b
res, ctx, use_model = chat(user_prompt="hello", model_store_entry=use_model, curr_ctx=ctx, verbose=True)

# escalate prompt -> llama3-70b
p = "Discuss the challenges and potential solutions for achieving sustainable development in the context of increasing global urbanization."
res, ctx, use_model = chat(user_prompt=p, model_store_entry=use_model, curr_ctx=ctx, verbose=True)

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