Mixtral-8x22B
This model checkpoint is provided as-is and might not be up-to-date. Please use the corresponding version from https://huggingface.co/mistralai org
MistralAI has uploaded weights to their organization at mistralai/Mixtral-8x22B-v0.1 and mistralai/Mixtral-8x22B-Instruct-v0.1 too.
Kudos to @v2ray for converting the checkpoints and uploading them in
transformers
compatible format. Go give them a follow!
Converted to HuggingFace Transformers format using the script here.
The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.
Run the model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistral-community/Mixtral-8x22B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
In half-precision
Note float16
precision only works on GPU devices
Click to expand
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistral-community/Mixtral-8x22B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Lower precision using (8-bit & 4-bit) using bitsandbytes
Click to expand
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistral-community/Mixtral-8x22B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Load the model with Flash Attention 2
Click to expand
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistral-community/Mixtral-8x22B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Notice
Mixtral-8x22B-v0.1 is a pretrained base model and therefore does not have any moderation mechanisms.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, LΓ©lio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, TimothΓ©e Lacroix, ThΓ©ophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 74.46 |
AI2 Reasoning Challenge (25-Shot) | 70.48 |
HellaSwag (10-Shot) | 88.73 |
MMLU (5-Shot) | 77.81 |
TruthfulQA (0-shot) | 51.08 |
Winogrande (5-shot) | 84.53 |
GSM8k (5-shot) | 74.15 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.480
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.730
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard77.810
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard51.080
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.530
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard74.150