Models/AliceAI Foundation 80B A3B Base

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AliceAI Foundation 80B A3B Base

AliceAI Foundation 80B A3B Base is an autoregressive text-generation base language model released by Yandex under the apache-2.0 license. It has a hybrid architecture with mixture-of-experts (MoE) layers: 80B total parameters, of which 3B are active per token, and a supported context length of up to 262,144 tokens. The model was trained from scratch, supports Russian and English, and is intended for use with Transformers and vLLM.

EstimatedRepository opened Sep 12, 2026Source checked 9/23/2026Version: 84105ba3

LOCALRENTED GPUOPEN WEIGHTS

Freshness 80 out of 100. Repository age: 57 days old, Sep 12, 2026. Measures age, not quality.

Popularity 49 out of 100. 2,375 downloads in 30 days; 409 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source

Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Nov 8, 2026. Statistics checked Nov 7, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters: 81.29B
Architecture: alice_ai
Context length: 262,144
License: apache-2.0
Disk space: 151.4 GB
Software: Transformers

View the model source

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Best for

Russian-language text generation and factual knowledge tasks
English-language text generation
Mathematical and reasoning tasks
Coding and programming tasks
Tool-interaction-related tasks

Tradeoffs

It is a base pretrained model, not fine-tuned for specific downstream tasks.
Benchmark comparisons are reported from internal infrastructure and should not be treated as independent verification.
No information is provided about safety alignment, bias evaluation, or content filtering.
Actual hardware requirements, inference speed, and memory usage are not stated in the supplied data.
No compatibility information is provided beyond the advertised Transformers and vLLM tags.

Ways to run it

Transformers · See official guide

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yandex/AliceAI-Foundation-80B-A3B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "Есть 256 монет с разным весом, за какое минимальное количество попарных взвешиваний можно найти вторую по весу монету?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=32768)
continuation_ids = output_ids[:, inputs.input_ids.shape[1] :]
print(tokenizer.decode(continuation_ids[0], skip_special_tokens=True))

Official instructions

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