Quasar — Unified Multimodal Research Prototype

Quasar is a small, custom PyTorch autoregressive model built on an Axiom-1.1 hybrid recurrence/attention backbone. Text, visual codes, audio codes, triangle meshes and tool-call proposals share one vocabulary and model. Frozen codecs convert between media and discrete tokens.

Release status: private, runnable research checkpoint release. The default root weights are the completed curriculum-research checkpoint shown below; the preserved v5 comparison weights are in variants/v5. Matching source code, text tokenizers and pinned frozen visual/audio codecs are included. Training datasets, optimizer states and an inference service are not included. This card describes these two snapshots as of 21 October 2026. Separate architecture experiments are still being evaluated and are excluded from these results.

Quality status: a focused research branch produces recognizable images and simple moving squares by reproducing memorized training examples. Unseen image generation and unfamiliar motion continuation remain unsuccessful. The research checkpoint was withheld from production because most prior-task losses worsened.

Model details

Property	Described v5 / curriculum-research model
Developer	Atlas-AI-research
Total model parameters	90,247,794, excluding frozen media codecs
Backbone	12 blocks, width 640; 9 recurrent blocks and 3 causal attention blocks
Attention	10 heads; full attention at zero-based blocks 3, 7 and 11
Feed-forward cells	Two dense 640 → 1280 → 640 experts per block; both are evaluated
Normalization / positions	RMSNorm, RoPE and learned visual spatial embeddings
Context	1,024 shared tokens
Vocabulary	54,144 shared IDs; 16,384-token text BPE; 32,768 visual codes
Images	Native 64×64 or 128×128; 64 or 256 visual tokens
Video	Up to 8 frames at native 64×64; 64 visual tokens/frame; per-frame codec
Audio interface	Four 1,024-entry DAC codebooks; short, complete utterances, at most 2 seconds
Mesh interface	Quantized coordinates and indexed triangle faces, up to 128 vertices/faces
Implementation	Custom Quasar PyTorch code; not a Transformers AutoModel release

The original text-stage model had 69,808,754 parameters. Vocabulary expansion and the later 128px spatial-embedding migration preserved learned weights while increasing the model to the size above. Quasar is not an Emu3 language-model fine-tune: Emu3 is used as a frozen pixel codec, not a generative backbone.

The Switch-style auxiliary term is constant for this two-of-two dense-cell setup; it does not demonstrate effective routing regularization. Reported validation CE excludes that auxiliary term.

Checkpoint identity

The downloadable weights match the exact artifacts measured below. SHA-256 values identify each unchanged model.safetensors file; packaging only makes metadata paths portable and supplies the matching runtime.

Artifact	Role	SHA-256
variants/v5/model.safetensors	Preserved v5 comparison model	e20242e9971b6a7cef7ca23a65c25faa70a007807a55a2c0cfe35f4f5682a643
Root model.safetensors	Unpromoted joint_repair/final research model	de4846e5e63c0004cf0b6331127ab421d6843f6efde04243c6d5448ab4545240

Intended use and limitations

Intended uses are small-model research, codec/packet interoperability studies, controlled experiments in visual conditioning, and investigation of recall, generalization and forgetting.

• The model is not validated for dependable factual answers, coding assistance, natural-scene image synthesis, natural video, intelligible generated speech, useful complex 3D assets or reliable tool selection.
• Tool outputs are schema-checked proposals. The model does not execute tools. Structural validity does not establish correct arguments or safe actions.
• The inference sampler forces packet boundaries and legal token ranges; those constraints are not evidence of learned semantic competence.
• Current video training uses procedurally controlled motion, not natural footage. Video annotation downloads were not treated as footage.
• These generation probes call explicit image/video APIs. They do not establish automatic routing of arbitrary chat requests to media generation.
• Real-photo outputs can reproduce training photographs. Creator attribution and original asset licenses remain relevant to reproduced images.
• English-biased source selection, small media corpora and synthetic tool data can introduce bias. No comprehensive safety, fairness or privacy audit was run.
• No user-supplied dataset was included; no user-data path was provided.

Training data

The admitted corpus combines retained built-in examples and selected external sources. A source appearing in a planning catalog does not mean it was trained. This list reflects admitted source families in the preserved training cache:

Domain	Sources and split controls
Text/chat	Pinned FineWeb-Edu; selected SmolTalk instruction, constraint, rewrite and summarization components; retained TinyStories, Dolly and built-in examples
Basic Python	Official MBPP full/train, 374 problems; official validation/test held out
Real photos	Open Images / Localized Narratives; per-image creator identity, eligible license, attribution and JPEG hashes checked; source-group splits retained
Video	Controlled local synthetic motion with source-group splits
Audio	Complete LibriTTS-R utterances ≤2 seconds; speaker-group and official split separation
Meshes	Admitted per-object CC0/CC-BY Objaverse/Cap3D assets; geometry validation and source-object grouping
Tool proposals	Safely parsed ToolACE examples with JSON Schema checks and tool-family/source grouping; no training-time tool execution

The shared text BPE was fit on training text only and remained frozen. The v5 cache contains 555,007 records. Checksum, token-range, grammar, context, offsets and exact-content/group-leakage validation passed. This is not a claim that all semantic near-duplicates have been ruled out.

The focused photo experiment uses only 16 reviewed training photos across cat/dog/car/flower, each with three short captions; 16 validation and 8 test photos retain their original source groups. The new motion cache admits 112 training, 29 validation and 29 test clips after frozen-codec calibration and code-sequence duplicate checks. All four colors × four directions are taught.

FineVideo and xLAM originals were access-gated and were not bypassed. Downloaded video alternatives supplied annotations, not usable footage. Broader catalog entries and raw alternatives are not claimed as trained capabilities.

Training procedure

The main curriculum used a nominal 300M padded-input text stage, followed by approximately 100M each for image, synthetic video, audio/mesh/tool, unified and later image-quality stages. The preserved v5 metadata records 800,882,688 cumulative padded input tokens, including pilot/history accounting. These are not counts of unique examples or supervised output tokens.

The completed media R&D session ran six experiments totaling 5,120 updates, 20,971,520 padded input tokens and 10,005,264 supervised targets. Independent branches do not all contribute to one checkpoint. The final shared research checkpoint adds 6,291,456 inputs along its actual ancestry after v5: a 4,194,304-input covered-motion branch, then a 2,097,152-input shared follow-up. Its final mixture is 45% focused photos, 35% controlled video generation, 10% continuation, 5% text and 5% chat.

Research training used one RTX 5070, BF16, AdamW, gradient checkpointing, Inductor, static 128/256/512/1024 buckets, and 1,024 padded inputs × 4 accumulation per update. Real batch shapes warmed through forward/backward without optimizer steps. Peak measured allocated training memory was 1.93 GiB. All six saved final models had finite weights, ordinary unwrapped serialization, matching recorded hashes and successful GPU reload/evaluation. The session took approximately 95 minutes including investigation, preparation, waiting, compilation, training and evaluation.

Evaluation results

Both models used identical prompts and seeds, the corrected cached visual-position inference path, temperature 0.5, top-k 64 and top-p 0.9. Photo subject matches were visually inspected. The controlled-motion metric checks object color, shape, size, background and direction; generated continuation suffixes are scored separately from their supplied prefixes.

Matched probe	Preserved v5	Shared research checkpoint
Recognizable requested photo category	0/24	23/24
New-wording photo prompts, subset of above	0/16	15/16
Closed color/direction motion, joint match	1/16	16/16
Unseen-trajectory continuation, joint suffix match	0/8	0/8

Recall caveat: all 24 final research photo samples and all 16 closed-motion clips exactly match training visual-code sequences. One cat request selects a flower. Novel wording can select an old prototype. These results do not establish new-photo synthesis, unseen composition or learned general motion dynamics. The motion descriptions are taught classes, even when their reference trajectory source groups are held out. No leaderboard or standard benchmark claim is made.

These are representative comparisons; the full photo evaluation includes one failed cat request. Display enlargement does not increase native resolution. Photo creators, original URLs and license links are recorded in the attribution manifest. Frozen-codec reconstruction and display resizing modify the original photographs. Procedural motion is CC0.

Generalization and retention

On the separate focused unseen-photo set, visual CE worsened from 8.2068 to 11.7081 on validation and 8.3475 to 11.7548 on test. On an identical common 52-batch prior-task screen, 11 of 13 losses worsened:

Task	v5 CE	Shared research CE
Text	3.9484	4.4183
Chat	1.6765	2.0703
Coding	1.7610	2.2605
Image generation	8.7170	11.6703
Video generation	5.1221	6.7368
Video continuation	6.2570	8.6859
Tool proposals	0.4624	0.8044

The common screen uses four deterministic held-out batches per task and ordinary unweighted CE without auxiliary terms. It is a small paired regression screen, not full-corpus semantic evaluation or a statistical significance claim. Lower mesh/audio losses on two tasks did not establish improved generated quality. The shared research checkpoint was not promoted. Exact identities, all 13 task losses and compact measurement receipts are in evaluation.json.

Frozen-codec reconstruction is a separate control: the existing 128px codec measured 22.2877 dB validation / 22.4653 dB test content PSNR. This predates the research session and is not generated-image quality. The earlier 64px real-photo probe measured 18.1176 dB; its original aspirational 20 dB gate remained false and was not relabeled as passed.

Download and run

This is a custom PyTorch checkpoint package, not a Transformers AutoModel. Use the included quasar implementation rather than AutoModel.from_pretrained("Atlas-AI-research/Quasar"). Authentication is needed while the repository is private. Preserve a working CUDA-compatible PyTorch installation; the measured environment used PyTorch 2.11.0+cu128. Install a suitable PyTorch build separately when needed, then install the remaining inference dependencies:

hf auth login
hf download Atlas-AI-research/Quasar --local-dir Quasar
cd Quasar
python -m pip install -r requirements-inference.txt
python verify_release.py --codecs
python infer.py --task image --prompt "make a picture of a cat" --output cat.png
python infer.py --task video --prompt "a small cyan square moving left on a gray background" --output motion.gif

The default inference device is CUDA; the CLI refuses a busy GPU. Verification uses CPU only. CPU inference is available with --device cpu; it does not train the model. Select the preserved baseline with --checkpoint variants/v5. CPU-only entry points hide CUDA and exclude the unused optional TorchAO quantizer within their process, avoiding GPU probing by an unrelated installed backend. The included package can also be imported directly from the downloaded directory:

from PIL import Image
from quasar.inference.extended import ExtendedGenerator
from quasar.inference.decoding import Sampling

# Root contains the exact research model shown in this card.
generator = ExtendedGenerator(".", "cuda")
sampling = Sampling(temperature=0.5, top_k=64, top_p=0.9, seed=20261021)
pixels = generator.generate_image("make a picture of a cat", sampling, resolution=128)
Image.fromarray(pixels).save("quasar_cat.png")
frames = generator.generate_video(
    "a small cyan square moving left on a gray background", 8, sampling
)

These examples do not establish acceptable output for arbitrary prompts. GPU use must be exclusive with local training/evaluation workers. No inference server is deployed. About 1.96 GiB of model/package files include both 361 MB Quasar weight files and one shared copy of each frozen codec; training data and optimizer states are omitted. The image/code mappings and original trained weights are unchanged.

The runtime is frozen from source commit 4afbdccdbd3b0967b161a68658cfd3b92c42636f, before the separate architecture experiments. Two packaging-only edits resolve visual/audio codec paths relative to the downloaded checkpoint. The included pinned visual codec Python is loaded locally with trust_remote_code=True; review it as executable source. Inference does not fetch additional model code or weights. Source identities and file hashes are in release.json and files.sha256.json. The packaged release passed CPU finite-forward/parameter/hash checks for both checkpoints and CPU encode/decode checks for both pinned codecs; see release_validation.json. This verification does not claim new semantic quality or replace the held-out results above.

Licensing and provenance

This private upload does not select a distribution license for Quasar's code or weights. No permissive license badge is implied by using PyTorch or permissively licensed components. Component notices remain separate and are included with their codec files; this upload does not make the repository public.

The existing frozen Emu3 visual tokenizer is pinned at revision c81f916ad371289e205310a7539255e8a9396488 under Apache-2.0. The local Descript DAC 24 kHz weights are pinned at 6ba020b5ba7d9d8076fb90db7e67f27e31980f6e; the project's official weights statement supplies MIT terms. These are component terms, not a Quasar weights license. Dataset, underlying page, photograph and per-object terms remain separate; for example, the FineWeb database license does not replace underlying web-page rights.

The visual component retains upstream copyright headers and Apache-2.0 terms in codecs/vision/LICENSE. The audio component retains the official MIT license and the pinned upstream weights statement under codecs/audio.

Reproducibility and next work

The completed R&D snapshot passed 146 CPU tests with 1 skip; the legacy integration module that performs additional optimizer training was excluded. This is evidence for that snapshot, not a test claim about unfinished architecture changes. Cached/full-prefix checks, source-weight preservation, frozen-cache validation, all-case visual review and checkpoint identities were recorded independently.

Next work should isolate caption clarity from repetition on matched examples, expand held-out position/speed and attribute coverage, and enforce stronger prior-task retention gates. Recognizable prototype recall alone is insufficient for releasing a general multimodal generator.

Model-card structure follows the Hugging Face model-card documentation.