Maxime Labonne mlabonne

## txtai-text-classify.py
import time

from datetime import timedelta

from datasets import load_dataset
from txtai import LLM
from txtai.pipeline import Labels, HFTrainer

def prompt(text):
    text = f"""

## YALL - Yet Another LLM Leaderboard.md

      
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                mlabonne
                / YALL - Yet Another LLM Leaderboard.md
            
            
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              July 9, 2024 09:28
            
              
                Leaderboard made with 🧐 LLM AutoEval (https://github.com/mlabonne/llm-autoeval) using Nous benchmark suite.
              
          
Model
Average
AGIEval
GPT4All
TruthfulQA
Bigbench


mlabonne/OmniTruthyBeagle-7B-v0 📄
57.8
45.72
77.49
76.16
50.18


mlabonne/NeuralOmniBeagle-7B-v2 📄
57.75
45.86
77.31
75.34
50.09


mlabonne/OmniBeagle-7B 📄
57.72
45.64
77.48
75.03
50.03


mlabonne/NeuralOmniBeagle-7B 📄
57.71
45.85
77.26
76.06
50.03


mlabonne/NeuralOmni-7B [📄](https://gist.github.com/mlabonne/4b5ecee86d0fd3714ba0cbd


## merge_peft.py
# Example usage:
# python merge_peft.py --base_model=meta-llama/Llama-2-7b-hf --peft_model=./qlora-out --hub_id=alpaca-qlora

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

import argparse

def get_args():
	import time

	from datetime import timedelta

	from datasets import load_dataset
	from txtai import LLM
	from txtai.pipeline import Labels, HFTrainer

	def prompt(text):
	text = f"""
Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/OmniTruthyBeagle-7B-v0 📄	57.8	45.72	77.49	76.16	50.18
mlabonne/NeuralOmniBeagle-7B-v2 📄	57.75	45.86	77.31	75.34	50.09
mlabonne/OmniBeagle-7B 📄	57.72	45.64	77.48	75.03	50.03
mlabonne/NeuralOmniBeagle-7B 📄	57.71	45.85	77.26	76.06	50.03
mlabonne/NeuralOmni-7B [📄](https://gist.github.com/mlabonne/4b5ecee86d0fd3714ba0cbd
	# Example usage:
	# python merge_peft.py --base_model=meta-llama/Llama-2-7b-hf --peft_model=./qlora-out --hub_id=alpaca-qlora

	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel
	import torch

	import argparse

	def get_args():