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- Mixtral_7Bx5_MoE_30B-Q3_K_L.gguf +3 -0
- Mixtral_7Bx5_MoE_30B-Q3_K_M.gguf +3 -0
- Mixtral_7Bx5_MoE_30B-Q3_K_S.gguf +3 -0
- Mixtral_7Bx5_MoE_30B-Q4_K_M.gguf +3 -0
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- Mixtral_7Bx5_MoE_30B-Q6_K.gguf +3 -0
- Mixtral_7Bx5_MoE_30B-Q8_0.gguf +3 -0
- README.md +67 -0
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README.md
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---
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license: cc-by-nc-4.0
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---
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# Mixtral MOE 5x7B
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MoE of the following models :
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* [Toten5/Marcoroni-neural-chat-7B-v1](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v1)
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* [NurtureAI/neural-chat-7b-v3-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-16k)
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* [mncai/mistral-7b-dpo-v6](https://huggingface.co/mncai/mistral-7b-dpo-v6)
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* [cookinai/CatMacaroni-Slerp](https://huggingface.co/cookinai/CatMacaroni-Slerp)
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* [ignos/Mistral-T5-7B-v1](https://huggingface.co/ignos/Mistral-T5-7B-v1)
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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_7Bx5_MoE_30B"
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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_7Bx5_MoE_30B"
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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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```
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