Quantization made by Richard Erkhov.

Mixtral-8x22B-v0.1 - GGUF

Model creator: https://huggingface.co/mistralai/
Original model: https://huggingface.co/mistralai/Mixtral-8x22B-v0.1/

Name	Quant method	Size
Mixtral-8x22B-v0.1.Q2_K.gguf	Q2_K	48.53GB
Mixtral-8x22B-v0.1.Q3_K_S.gguf	Q3_K_S	57.28GB
Mixtral-8x22B-v0.1.Q3_K.gguf	Q3_K	63.14GB
Mixtral-8x22B-v0.1.Q3_K_M.gguf	Q3_K_M	63.14GB
Mixtral-8x22B-v0.1.Q3_K_L.gguf	Q3_K_L	67.6GB
Mixtral-8x22B-v0.1.IQ4_XS.gguf	IQ4_XS	71.12GB
Mixtral-8x22B-v0.1.Q4_0.gguf	Q4_0	74.05GB
Mixtral-8x22B-v0.1.IQ4_NL.gguf	IQ4_NL	34.53GB
Mixtral-8x22B-v0.1.Q4_K_S.gguf	Q4_K_S	74.96GB
Mixtral-8x22B-v0.1.Q4_K.gguf	Q4_K	45.38GB
Mixtral-8x22B-v0.1.Q4_K_M.gguf	Q4_K_M	79.71GB
Mixtral-8x22B-v0.1.Q4_1.gguf	Q4_1	82.19GB
Mixtral-8x22B-v0.1.Q5_0.gguf	Q5_0	90.32GB
Mixtral-8x22B-v0.1.Q5_K_S.gguf	Q5_K_S	90.32GB
Mixtral-8x22B-v0.1.Q5_K.gguf	Q5_K	93.11GB
Mixtral-8x22B-v0.1.Q5_K_M.gguf	Q5_K_M	93.11GB
Mixtral-8x22B-v0.1.Q5_1.gguf	Q5_1	98.45GB
Mixtral-8x22B-v0.1.Q6_K.gguf	Q6_K	107.6GB
Mixtral-8x22B-v0.1.Q8_0.gguf	Q8_0	139.16GB

Original model description:

language:

fr
it
de
es
en license: apache-2.0 tags:
moe

extra_gated_description: If you want to learn more about how we process your personal data, please read our Privacy Policy.

Model Card for Mixtral-8x22B

The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.

For full details of this model please read our release blog post.

Warning

This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different.

Run the model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/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:

Notice

Mixtral-8x22B 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, Lélio 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, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall