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mimir-lcm/
Mimir-1.6B-Instruct

Name: Mimir-1.6B-Instruct
Availability: InStock

2605.25263

mit

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Model Card for Mimir-1.6B-Instruct

How to Get Started with the Model

Below you can find an example of model usage. To facilitate its usage, we recommend to follow these steps:

huggingface-cli download mimir-lcm/Mimir-1.6B-Instruct --local-dir mimir-lcm/Mimir-1.6B-Instruct 
git clone https://github.com/facebookresearch/large_concept_model.git
mv large_concept_model/lcm .
pip install torch==2.5.1 --extra-index-url https://download.pytorch.org/whl/cu121 --upgrade
pip install fairseq2==v0.3.0rc1 --pre --extra-index-url  https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cu121 --upgrade
pip install omegaconf==2.3.0
pip install sonar-space==0.3.2
pip install wtpsplit==2.1.2

Now you should be able to run the following:

python

import lcm
import torch
from pathlib import Path

from lcm.models.two_tower_diffusion_lcm.builder import (
    create_two_tower_diffusion_lcm_model,
)
from lcm.models.two_tower_diffusion_lcm.archs import two_tower_diffusion_lcm_1_6B
from lcm.inference.two_tower_diffusion_lcm.generator import (
    TwoTowerDiffusionLCMGenerator,
    DiffusionLCMGeneratorOptions,
)
from lcm.datasets.batch import EmbeddingsBatch
from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline, EmbeddingToTextModelPipeline

from wtpsplit import SaT

lcm.setup_fairseq2()

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

from lcm.models.two_tower_diffusion_lcm.builder import TwoTowerDiffusionLCModel

_original_sample_fn = TwoTowerDiffusionLCModel.sample_initial_noise_vectors

def _patched_sample_fn(self, batch_size: int):
    latents = _original_sample_fn(self, batch_size)
    return latents.to(dtype=self.dtype)

TwoTowerDiffusionLCModel.sample_initial_noise_vectors = _patched_sample_fn

CHECKPOINT_PATH = "mimir-lcm/Mimir-1.6B-Instruct/model.pt"
INFERENCE_DTYPE = torch.float16

TEXT_DECODER = EmbeddingToTextModelPipeline(decoder="text_sonar_basic_decoder", tokenizer="text_sonar_basic_decoder", device=torch.device(DEVICE))
TEXT_EMBEDDER = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder", device=torch.device(DEVICE))

def decode_embeddings(embeddings):
    
    embeddings = embeddings.to(device=DEVICE, dtype=torch.float32)
    
    print("Decoding...")
    results = TEXT_DECODER.predict(
        embeddings, 
        target_lang="eng_Latn"
    )
    
    return results

def get_eos_vector():
    return TEXT_EMBEDDER.predict(["End of text."], source_lang="eng_Latn").squeeze().to(device=DEVICE, dtype=INFERENCE_DTYPE)

def load_two_tower_model(checkpoint_path, device="cuda"):
    
    config = two_tower_diffusion_lcm_1_6B()

    print("Building model structure...")
    model = create_two_tower_diffusion_lcm_model(
        config, 
        device=torch.device(device),
        dtype=INFERENCE_DTYPE
    )

    print(f"Loading weights from {checkpoint_path}...")
    state_dict = torch.load(checkpoint_path, map_location=device)
    
    if "model" in state_dict:
        state_dict = state_dict["model"]
        
    model.load_state_dict(state_dict, strict=True)
    
    model.eval()
    model.to(device=DEVICE, dtype=INFERENCE_DTYPE)
    print("Model loaded successfully.")
    return model

def run_inference(model, prompt_embeddings, device="cuda"):

    options = DiffusionLCMGeneratorOptions(
        eos_threshold=0.9,
        inference_timesteps=40,
        initial_noise_scale=0.6,
        guidance_scale=1.5,
        guidance_rescale=0.7,
        epsilon_scaling=1.00045,
        stop_on_repetition_cosine_threshold=0.9,
        seed=42,
    )
    
    generator = TwoTowerDiffusionLCMGenerator(model, options, eos_vec=get_eos_vector())

    seqs = prompt_embeddings.to(device)
    batch_input = EmbeddingsBatch(seqs=seqs, padding_mask=None)

    print("Running generation...")
    output = generator(batch_input)
    
    return output

if __name__ == "__main__":

    raw_prompt_text = "User turn.\n\nJohn lives in his house and loves to play soccer.\n\nGive a brief definition of the word \"house\" in the sentence given as input. Generate only the definition.\n\nAssistant turn."

    model = load_two_tower_model(CHECKPOINT_PATH, DEVICE)
    
    with torch.no_grad():

        sat_model = SaT("segment-any-text/sat-3l")
        if torch.cuda.is_available():
            sat_model.half().to(DEVICE)
            
        split_outputs = list(sat_model.split([raw_prompt_text], threshold=0.02))
        sentences = [s.strip() for s in split_outputs[0] if s.strip()]

        print(sentences)

        prompt = TEXT_EMBEDDER.predict(sentences, source_lang="eng_Latn", batch_size=1024)
        prompt = prompt.to(device=DEVICE, dtype=INFERENCE_DTYPE)
        prompt = prompt.unsqueeze(0)
    
    results = run_inference(model, prompt, DEVICE)
    
    for j, hyp in enumerate(results.hypotheses[0]):
        print(decode_embeddings(hyp.seq)[prompt.shape[1]:])

Make sure to change the source language from "eng_Latn" to the one you want to perform inference with.

Furthermore, for the instruct model, we used the following mappings for the "User turn." and "Assistant turn." strings.

python

USER_TRANSLATION = {
    "arb_Arab": "دور المستخدم.", 
    "bel_Cyrl": "Ход карыстальніка.", 
    "ben_Beng": "ব্যবহারকারীর পালা।",
    "bos_Latn": "Red korisnika.", 
    "bul_Cyrl": "Ред е на потребителя.", 
    "cat_Latn": "Torn de l'usuari.",
    "ces_Latn": "Je řada na uživateli.", 
    "cym_Latn": "Tro'r defnyddiwr.", 
    "dan_Latn": "Brugerens tur.",
    "deu_Latn": "Der Benutzer ist am Zug.", 
    "eng_Latn": "User turn.", 
    "fra_Latn": "C'est au tour de l'utilisateur.",
    "heb_Hebr": "תור המשתמש.", 
    "hin_Deva": "उपयोगकर्ता की बारी।", 
    "hrv_Latn": "Poteg korisnika.",
    "ind_Latn": "Giliran pengguna.", 
    "jpn_Jpan": "ユーザーの番です。", 
    "ita_Latn": "È il turno dell'utente.",
    "kan_Knda": "ಬಳಕೆದಾರರ ತಿರುವು.", 
    "kor_Hang": "사용자 차례입니다.", 
    "lvs_Latn": "Lietotāja kārta.",
    "mal_Mlym": "ഉപയോക്താവിന്റെ ഊഴം",
    "mar_Deva": "वापरकर्त्याची पाळी.",
    "mkd_Cyrl": "Потребен е корисник.",
    "npi_Deva": "प्रयोगकर्ताको पालो।",
    "nld_Latn": "De gebruiker is aan de beurt.",
    "ory_Orya": "ୟୁଜର୍ ଟର୍ନ୍ |",
    "pol_Latn": "Ruch użytkownika.",
    "por_Latn": "É a vez do utilizador.",
    "ron_Latn": "E rândul utilizatorului.",
    "rus_Cyrl": "Ход пользователя.",
    "slk_Latn": "Je na ťa.",
    "slv_Latn": "Na vrsti je uporabnik.",
    "srp_Cyrl": "Кориснички потез.",
    "spa_Latn": "Turno del usuario.",
    "swe_Latn": "Användarens tur.",
    "swh_Latn": "Mzunguko wa mtumiaji.",
    "tam_Taml": "பயனர் முறை.",
    "tel_Telu": "వినియోగదారుని వంతు",
    "tha_Thai": "ผู้ใช้เทิร์น",
    "tur_Latn": "Kullanıcı sırası.",
    "ukr_Cyrl": "Хід користувача.",
    "urd_Arab": "صارف کی باری۔",
    "vie_Latn": "Đến lượt người dùng.",
    "zho_Hans": "轮到用户了。"
}

python

ASSISTANT_TRANSLATION = {
    "arb_Arab": "دور المساعد.", 
    "bel_Cyrl": "Памочнік, свой ход.", 
    "ben_Beng": "সহকারী পালা।",
    "bos_Latn": "Pomoćni potez.", 
    "bul_Cyrl": "Ред е на помощника.", 
    "cat_Latn": "Torn de l'ajudant.",
    "ces_Latn": "Na řadě je asistent.", 
    "cym_Latn": "Tro'r cynorthwyydd.", 
    "dan_Latn": "Assistenten er på tur.",
    "deu_Latn": "Der Assistent ist an der Reihe.", 
    "eng_Latn": "Assistant turn.", 
    "fra_Latn": "C'est au tour de l'assistant.",
    "heb_Hebr": "תורו של העוזר.", 
    "hin_Deva": "सहायक की बारी।", 
    "hrv_Latn": "Pomoćni potez.",
    "ind_Latn": "Giliran asisten.", 
    "jpn_Jpan": "アシスタントの番。", 
    "ita_Latn": "È il turno dell'assistente.",
    "kan_Knda": "ಸಹಾಯಕ ತಿರುವು.", 
    "kor_Hang": "조수 차례.", 
    "lvs_Latn": "Palīga kārta.",
    "mal_Mlym": "സഹായിയുടെ ഊഴം",
    "mar_Deva": "सहाय्यक पालवी.",
    "mkd_Cyrl": "Помошник-ред.",
    "npi_Deva": "सहायक पालो।",
    "nld_Latn": "De assistent is aan de beurt.",
    "ory_Orya": "ଆସିଷ୍ଟାଣ୍ଟ ଟର୍ନ୍ |",
    "pol_Latn": "Rzuty pomocników.",
    "por_Latn": "É a vez do assistente.",
    "ron_Latn": "E rândul asistentului.",
    "rus_Cyrl": "Очередь помощника.",
    "slk_Latn": "Na rade je asistent.",
    "slv_Latn": "Na vrsti je pomočnik.",
    "srp_Cyrl": "Помоћни круг.",
    "spa_Latn": "Turno del asistente.",
    "swe_Latn": "Assistenten är på tur.",
    "swh_Latn": "Mzunguko wa msaidizi.",
    "tam_Taml": "துணைவரின் முறை.",
    "tel_Telu": "సహాయకుడి వంతు",
    "tha_Thai": "ผู้ช่วยเลี้ยว",
    "tur_Latn": "Sıra asistanında.",
    "ukr_Cyrl": "Черга помічника.",
    "urd_Arab": "معاون کی باری۔",
    "vie_Latn": "Đến lượt trợ lý.",
    "zho_Hans": "助手的回合。"
}

Citation

If you use this model in your research, please cite the following:

bibtex

@misc{musacchio2026mimirlargescalemultilingualconcept,
      title={Mimir: Large-scale Multilingual Concept Modeling}, 
      author={Elio Musacchio and Lucia Siciliani and Pierpaolo Basile},
      year={2026},
      eprint={2605.25263},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.25263}, 
}