Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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Mixtral_11Bx2_MoE_19B - GGUF
- Model creator: https://huggingface.co/cloudyu/
- Original model: https://huggingface.co/cloudyu/Mixtral_11Bx2_MoE_19B/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Mixtral_11Bx2_MoE_19B.Q2_K.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q2_K.gguf) | Q2_K | 6.58GB |
| [Mixtral_11Bx2_MoE_19B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.IQ3_XS.gguf) | IQ3_XS | 7.34GB |
| [Mixtral_11Bx2_MoE_19B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.IQ3_S.gguf) | IQ3_S | 7.75GB |
| [Mixtral_11Bx2_MoE_19B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q3_K_S.gguf) | Q3_K_S | 7.73GB |
| [Mixtral_11Bx2_MoE_19B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.IQ3_M.gguf) | IQ3_M | 7.94GB |
| [Mixtral_11Bx2_MoE_19B.Q3_K.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q3_K.gguf) | Q3_K | 8.59GB |
| [Mixtral_11Bx2_MoE_19B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q3_K_M.gguf) | Q3_K_M | 8.59GB |
| [Mixtral_11Bx2_MoE_19B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q3_K_L.gguf) | Q3_K_L | 9.32GB |
| [Mixtral_11Bx2_MoE_19B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.IQ4_XS.gguf) | IQ4_XS | 9.66GB |
| [Mixtral_11Bx2_MoE_19B.Q4_0.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q4_0.gguf) | Q4_0 | 10.09GB |
| [Mixtral_11Bx2_MoE_19B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.IQ4_NL.gguf) | IQ4_NL | 10.19GB |
| [Mixtral_11Bx2_MoE_19B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q4_K_S.gguf) | Q4_K_S | 10.17GB |
| [Mixtral_11Bx2_MoE_19B.Q4_K.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q4_K.gguf) | Q4_K | 10.79GB |
| [Mixtral_11Bx2_MoE_19B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q4_K_M.gguf) | Q4_K_M | 10.79GB |
| [Mixtral_11Bx2_MoE_19B.Q4_1.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q4_1.gguf) | Q4_1 | 11.19GB |
| [Mixtral_11Bx2_MoE_19B.Q5_0.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q5_0.gguf) | Q5_0 | 12.3GB |
| [Mixtral_11Bx2_MoE_19B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q5_K_S.gguf) | Q5_K_S | 12.3GB |
| [Mixtral_11Bx2_MoE_19B.Q5_K.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q5_K.gguf) | Q5_K | 12.67GB |
| [Mixtral_11Bx2_MoE_19B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q5_K_M.gguf) | Q5_K_M | 12.67GB |
| [Mixtral_11Bx2_MoE_19B.Q5_1.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q5_1.gguf) | Q5_1 | 13.41GB |
| [Mixtral_11Bx2_MoE_19B.Q6_K.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q6_K.gguf) | Q6_K | 14.66GB |
| [Mixtral_11Bx2_MoE_19B.Q8_0.gguf](https://huggingface.co/RichardErkhov/cloudyu_-_Mixtral_11Bx2_MoE_19B-gguf/blob/main/Mixtral_11Bx2_MoE_19B.Q8_0.gguf) | Q8_0 | 18.99GB |




Original model description:
---
  license: cc-by-nc-4.0
---


# Mixtral MOE 2x10.7B

[One of Best MoE Model reviewd by reddit community](https://www.reddit.com/r/LocalLLaMA/comments/1916896/llm_comparisontest_confirm_leaderboard_big_news/)

MoE  of the following models :


* [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
* [jeonsworld/CarbonVillain-en-10.7B-v1](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v1)


* Local Test
* hf (pretrained=cloudyu/Mixtral_11Bx2_MoE_19B), gen_kwargs: (None), limit: None, num_fewshot: 10, batch_size: auto (32)
|  Tasks  |Version|Filter|n-shot| Metric |Value |   |Stderr|
|---------|-------|------|-----:|--------|-----:|---|-----:|
|hellaswag|Yaml   |none  |    10|acc     |0.7142|±  |0.0045|
|         |       |none  |    10|acc_norm|0.8819|±  |0.0032|


gpu code example

```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math

## v2 models
model_path = "cloudyu/Mixtral_11Bx2_MoE_19B"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
  input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

  generation_output = model.generate(
    input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
  )
  print(tokenizer.decode(generation_output[0]))
  prompt = input("please input prompt:")
```

CPU example

```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math

## v2 models
model_path = "cloudyu/Mixtral_11Bx2_MoE_19B"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
  input_ids = tokenizer(prompt, return_tensors="pt").input_ids

  generation_output = model.generate(
    input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
  )
  print(tokenizer.decode(generation_output[0]))
  prompt = input("please input prompt:")

```