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license: cc-by-nc-4.0 |
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--- |
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# Mixtral MOE 2x10.7B |
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[One of Best MoE Model reviewd by reddit community](https://www.reddit.com/r/LocalLLaMA/comments/1916896/llm_comparisontest_confirm_leaderboard_big_news/) |
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MoE of the following models : |
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* [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) |
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* [jeonsworld/CarbonVillain-en-10.7B-v1](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v1) |
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* Local Test |
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* hf (pretrained=cloudyu/Mixtral_11Bx2_MoE_19B), gen_kwargs: (None), limit: None, num_fewshot: 10, batch_size: auto (32) |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|---------|-------|------|-----:|--------|-----:|---|-----:| |
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|hellaswag|Yaml |none | 10|acc |0.7142|± |0.0045| |
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| | |none | 10|acc_norm|0.8819|± |0.0032| |
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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_11Bx2_MoE_19B" |
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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_11Bx2_MoE_19B" |
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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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``` |
