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---
license: llama2
---
# LM-cocktail 10.7B v1
This is a 50%-50% model of the SOLAR model and meow.
https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0
https://huggingface.co/rishiraj/meow
who rank #1 and #2 among models <13B in the https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard by 2023/12/20.
# Alpaca Eval
I am thrilled to announce that ChatGPT has ranked LMCocktail 10.7B as the second best model next to GPT4 on AlpcaEval in my local community run. You can also check the leaderboard at [./alpaca_eval/chatgpt_fn_--SOLAR-10-7B-LMCocktail/](./alpaca_eval/chatgpt_fn_--SOLAR-10-7B-LMCocktail/)
```
win_rate standard_error n_total avg_length
gpt4 73.79 1.54 805 1365
SOLAR-10.7B-LMCocktail(new)73.45 1.56 804 1203
claude 70.37 1.60 805 1082
chatgpt 66.09 1.66 805 811
wizardlm-13b 65.16 1.67 805 985
vicuna-13b 64.10 1.69 805 1037
guanaco-65b 62.36 1.71 805 1249
oasst-rlhf-llama-33b 62.05 1.71 805 1079
alpaca-farm-ppo-human 60.25 1.72 805 803
falcon-40b-instruct 56.52 1.74 805 662
text_davinci_003 50.00 0.00 805 307
alpaca-7b 45.22 1.74 805 396
text_davinci_001 28.07 1.56 805 296
```
# Code
The LM-cocktail is novel technique for merging multiple models https://arxiv.org/abs/2311.13534
Code is backed up by this repo https://github.com/FlagOpen/FlagEmbedding.git
Merging scripts available under the [./scripts](./scripts) folder
# Result
The SOLAR model is the first model <30B that can answer this question from my test:
```
What will AI be like in the year 1010 A.D?
```
without hullicinating into 1010 A.D is a future time (like other llama2 models)
Models greater than that, like Yi-34B could answer this paradoxic question correctly as well, since it is huge enough.
### SOLAR 10.7B output
![img](./assets/SOLAR.png)
### LMCocktail 10.7B output1
![img](./assets/SOLAR_mixed.png)
### LMCocktail 10.7B output2
![img](./assets/SOLAR_mixed2.png)

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,win_rate,standard_error,n_wins,n_wins_base,n_draws,n_total,mode,avg_length
gpt4,73.7888198757764,1.5359801545073597,588,205,12,805,minimal,1365
SOLAR-10.7B-LMCocktail,73.44527363184079,1.5572150363643398,590,213,1,804,community,1203
claude,70.37267080745342,1.599519507147828,562,234,9,805,minimal,1082
chatgpt,66.08695652173913,1.6626479994330317,529,270,6,805,minimal,811
wizardlm-13b,65.15527950310559,1.670034107787565,520,276,9,805,minimal,985
vicuna-13b,64.09937888198758,1.6895185863153146,515,288,2,805,minimal,1037
guanaco-65b,62.36024844720497,1.7086348811605765,502,303,0,805,minimal,1249
oasst-rlhf-llama-33b,62.0496894409938,1.7080028976103514,498,304,3,805,minimal,1079
alpaca-farm-ppo-human,60.24844720496895,1.7169496733548772,481,316,8,805,minimal,803
falcon-40b-instruct,56.52173913043478,1.7438750520312944,453,348,4,805,minimal,662
phi-2-alpaca-gpt4-dpo,55.59701492537313,1.7533719245384989,447,357,0,804,community,4532
text_davinci_003,50.0,0.0,0,0,805,805,minimal,307
alpaca-7b,45.21739130434783,1.7375846781579476,356,433,16,805,minimal,396
text_davinci_001,28.07453416149068,1.5602183426587484,216,569,20,805,minimal,296
1 win_rate standard_error n_wins n_wins_base n_draws n_total mode avg_length
2 gpt4 73.7888198757764 1.5359801545073597 588 205 12 805 minimal 1365
3 SOLAR-10.7B-LMCocktail 73.44527363184079 1.5572150363643398 590 213 1 804 community 1203
4 claude 70.37267080745342 1.599519507147828 562 234 9 805 minimal 1082
5 chatgpt 66.08695652173913 1.6626479994330317 529 270 6 805 minimal 811
6 wizardlm-13b 65.15527950310559 1.670034107787565 520 276 9 805 minimal 985
7 vicuna-13b 64.09937888198758 1.6895185863153146 515 288 2 805 minimal 1037
8 guanaco-65b 62.36024844720497 1.7086348811605765 502 303 0 805 minimal 1249
9 oasst-rlhf-llama-33b 62.0496894409938 1.7080028976103514 498 304 3 805 minimal 1079
10 alpaca-farm-ppo-human 60.24844720496895 1.7169496733548772 481 316 8 805 minimal 803
11 falcon-40b-instruct 56.52173913043478 1.7438750520312944 453 348 4 805 minimal 662
12 phi-2-alpaca-gpt4-dpo 55.59701492537313 1.7533719245384989 447 357 0 804 community 4532
13 text_davinci_003 50.0 0.0 0 0 805 805 minimal 307
14 alpaca-7b 45.21739130434783 1.7375846781579476 356 433 16 805 minimal 396
15 text_davinci_001 28.07453416149068 1.5602183426587484 216 569 20 805 minimal 296

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}
}

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sciprts/convert.py Normal file
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from transformers import AutoModelForCausalLM
import torch
import torch.utils.dlpack
# Load the original model
model_name = "./mixed_llm"
model = AutoModelForCausalLM.from_pretrained(model_name)
# Convert the model to a different precision
model = model.half()
# Save the model as a safetensor
model.save_pretrained(f"./mixed_llm_half", safetensors=True)

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sciprts/merge.log Normal file
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sciprts/merge.py Normal file
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from LM_Cocktail import mix_models_by_layers
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", type=str, default="decoder", help="Type of model to be mixed")
parser.add_argument("--output_path", type=str, default="./mixed_llm", help="Path to save the mixed model")
parser.add_argument("--max_length", type=int, default=100, help="Maximum length of the sequence to be generated")
parser.add_argument("--models", type=str, nargs='+', default=["meta-llama/Llama-2-7b-chat-hf", "Shitao/llama2-ag-news"], help="Path to the models to be mixed")
parser.add_argument("--weights", type=float, nargs='+', default=[0.7, 0.3], help="Weights of the models to be mixed")
parser.add_argument("--save_precision", type=str, default='float32', help="mixed model saved format")
args = parser.parse_args()
# Mix Large Language Models (LLMs) and save the combined model to the path: ./mixed_llm
model = mix_models_by_layers(
model_names_or_paths=args.models,
model_type=args.model_type,
weights=args.weights,
output_path=args.output_path)
print(model)

8
sciprts/merge.sh Executable file
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#!/bin/bash
eval "$(conda shell.bash hook)"
conda activate llmcocktail
CUDA_VISIBLE_DEVICES=1 python merge.py \
--models /media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/meow /media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/SOLAR-10.7B-Instruct-v1.0 \
--weights 0.5 0.5 \
|& tee ./merge.log

30
special_tokens_map.json Normal file
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{
"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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43
tokenizer_config.json Normal file
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{
"add_bos_token": true,
"add_eos_token": false,
"added_tokens_decoder": {
"0": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": [],
"bos_token": "<s>",
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{% if message['content']%}{{'### System:\n' + message['content']+'\n\n'}}{% endif %}{% elif message['role'] == 'user' %}{{'### User:\n' + message['content']+'\n\n'}}{% elif message['role'] == 'assistant' %}{{'### Assistant:\n' + message['content']}}{% endif %}{% if loop.last and add_generation_prompt %}{{ '### Assistant:\n' }}{% endif %}{% endfor %}",
"clean_up_tokenization_spaces": false,
"eos_token": "</s>",
"legacy": true,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "</s>",
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"tokenizer_class": "LlamaTokenizer",
"unk_token": "<unk>",
"use_default_system_prompt": true
}