prince-canuma / Mixtral-8x22B-v0.1-4bit

Model Card for Mixtral-8x22B

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

Model details:

• 🧠 ~176B params, ~44B active during inference
• 🪟 65K context window
• 🕵🏾‍♂️ 8 experts, 2 per token
• 🤓 32K vocab size
• ✂️ Similar tokenizer as 7B

Model quantized and added by Prince Canuma using the full-precision model here: v2ray/Mixtral-8x22B-v0.1 .

Run the model in 4-bit precision

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "prince-canuma/Mixtral-8x22B-v0.1-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Who is Einstein?"
inputs = tokenizer(text, return_tensors="pt")

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, 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.