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--- |
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license: cc-by-nc-4.0 |
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--- |
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# Mixtral MOE 4x7B |
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MOE the following models by mergekit: |
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* [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co./Q-bert/MetaMath-Cybertron-Starling) |
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* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) |
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* [teknium/Mistral-Trismegistus-7B](https://huggingface.co./teknium/Mistral-Trismegistus-7B) |
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* [meta-math/MetaMath-Mistral-7B](https://huggingface.co./meta-math/MetaMath-Mistral-7B) |
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* [openchat/openchat-3.5-1210](https://huggingface.co./openchat/openchat-3.5-1210) |
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Metrics |
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* Average : 68.85 |
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* ARC:65.36 |
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* HellaSwag:85.23 |
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* more details: https://huggingface.co./datasets/open-llm-leaderboard/results/blob/main/cloudyu/Mixtral_7Bx4_MOE_24B/results_2023-12-23T18-05-51.243288.json |
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gpu code example |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import math |
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## v2 models |
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model_path = "cloudyu/Mixtral_7Bx4_MOE_24B" |
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True |
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) |
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print(model) |
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prompt = input("please input prompt:") |
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while len(prompt) > 0: |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda") |
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generation_output = model.generate( |
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input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2 |
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) |
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print(tokenizer.decode(generation_output[0])) |
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prompt = input("please input prompt:") |
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``` |
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CPU example |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import math |
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## v2 models |
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model_path = "cloudyu/Mixtral_7Bx4_MOE_24B" |
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False |
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) |
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print(model) |
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prompt = input("please input prompt:") |
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while len(prompt) > 0: |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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generation_output = model.generate( |
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input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2 |
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) |
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print(tokenizer.decode(generation_output[0])) |
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prompt = input("please input prompt:") |
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``` |
