| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| tags: |
| - world-model |
| - video-generation |
| - rocket-league |
| - interactive |
| library_name: mira |
| pipeline_tag: other |
| --- |
| |
| # Model Card for MIRA Mini PSD |
|
|
| The few-step variant of MIRA Mini: the same 1B action-conditioned world model of |
| Rocket League, distilled so that two sampling steps replace eight. The sampler does |
| 4x less work per frame; delivered frame rate on the same GPU is about 1.3x (14.5 to |
| 19.4 fps measured on L40S) because decode still dominates, until the small decoder |
| is swapped in. Visual quality matched the 8-step baseline on our evaluations. |
|
|
| > Built on [MIRA](https://mira-wm.com/), released July 6, 2026 by |
| > [General Intuition](https://www.generalintuition.com/) and [Kyutai](https://kyutai.org/) |
| > with Epic Games: [code](https://github.com/mira-wm/mira), |
| > [dataset](https://huggingface.co/datasets/kyutai/rocket-science), and a published training |
| > recipe. MIRA Mini is Alakazam's independent reproduction and optimization of that work. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| Identical architecture to [alakazamworld/mira-mini](https://huggingface.co/alakazamworld/mira-mini) |
| (a 1B diffusion transformer in the latent space of a representation-autoencoder codec), |
| plus the paper's progressive self-distillation (PSD), applied post hoc. Sampling a latent |
| frame integrates the flow-matching field; PSD distills the model so one large integration |
| step replaces two smaller ones, which lets two steps reach the quality that previously |
| took eight. |
|
|
| The paper distills its pretrained model and leaves unspecified how the step-size (Δ) |
| conditioning pathway is introduced into a checkpoint trained without one. This model uses |
| our construction: the pathway is injected with a zero-initialized output projection, so at |
| step 0 the model is identical to the base checkpoint, and the distillation signal fades in |
| from there (10k steps, learning rate 3e-5, warmup 100; the from-scratch learning rate |
| diverges on a converged checkpoint). |
|
|
| - **Developed by:** Alakazam |
| - **Model type:** Action-conditioned world model (interactive video generation) |
| - **License:** CC BY-NC-SA 4.0, inherited from the training dataset |
| - **Reproduction of:** MIRA (General Intuition and Kyutai, with Epic Games) |
|
|
| **This model is for demonstration and research only.** The training dataset |
| ([kyutai/rocket-science](https://huggingface.co/datasets/kyutai/rocket-science)) is |
| CC BY-NC-SA 4.0, with Rocket League content used by Epic Games' permission. These weights |
| inherit that license: non-commercial, share-alike, with attribution. |
|
|
| These weights are an independent release by Alakazam. They are not released by, associated with, or endorsed by General Intuition, Kyutai, or Epic Games. |
|
|
| ### Model Sources |
|
|
| - **Player (one command):** [Alakazam-studios/alakazam-mira-mini](https://github.com/Alakazam-studios/alakazam-mira-mini) |
| - **Technical report:** [alakazam.gg/mira-mini](https://alakazam.gg/mira-mini) |
| - **Base 1B model:** [alakazamworld/mira-mini](https://huggingface.co/alakazamworld/mira-mini) |
| - **Upstream release:** [mira-wm/mira](https://github.com/mira-wm/mira) |
|
|
| ## How to Get Started |
|
|
| ```bash |
| pip install alakazam-mira-mini |
| mira-mini play --model mira-mini-psd |
| ``` |
|
|
| The bundle is `world_model_config.yaml`, `checkpoint-10000/checkpoint.pth`, `codec/`, |
| `context/default.npz`. Two steps is the intended setting; 4 and 8 also work (quality is |
| flat across them after distillation, so extra steps mostly cost frame rate). |
|
|
| ## Training Details |
|
|
| Post-hoc PSD fine-tune of the mira-mini 1B checkpoint: 10k steps on 8 preemptible H100s, |
| PSD loss applied stochastically on 10% of updates (the paper's mixing rate), ground-truth |
| flow term otherwise. Validation loss improved from 0.3952 (base, at handoff) to 0.3741, |
| and the 2-step teacher-forced LPIPS ends below the base model's 2-step LPIPS with the |
| margin still growing at 10k (−0.0021 on our held-out shard). |
|
|
| Training data (through the base model): [kyutai/rocket-science](https://huggingface.co/datasets/kyutai/rocket-science), |
| as released, no additions. |
|
|
| ## Performance |
|
|
| | Hardware | 8 steps (base) | 2 steps (this model) | |
| |---|---|---| |
| | L40S | 14.5 fps | 19.4 fps measured (live serving probe) | |
| | B200 | 25.7 fps | not benched at 2 steps | |
| | L4 | 5.4 fps | not benched at 2 steps | |
|
|
| Measured numbers come from serving probes on the stated hardware; estimates are marked. |
| The serving runtime is the FlashDreams CUDA-graph port (bit-exact against the reference |
| implementation at matched settings). |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - Same limitations as the base model: bot-collected Rocket League only, no transfer, |
| plausible continuations rather than exact physics. |
| - Few-step sampling drifts slightly more per step than 8-step sampling on long rollouts; |
| quantified in the report's physics-verification section: in ten-minute rollouts the |
| distilled rungs ride an elevated-but-flat distance band (a level shift, not progressive |
| melting), and per-action recoverability degrades tail-first (rare actions like air-rolls |
| before common ones) — aggregate numbers hide exactly that. |
| - Non-commercial license, inherited from the dataset. |
|
|
| ## Citation |
|
|
| Cite the MIRA paper and link this repository. |
|
|
| ```bibtex |
| @article{hu2026mira, |
| title = {Multiplayer Interactive World Models with Representation Autoencoders}, |
| author = {Hu, Anthony and others}, |
| year = {2026}, |
| note = {arXiv:2607.05352} |
| } |
| ``` |
|
|
| ## Model Card Authors |
|
|
| Alakazam (alakazam.gg) |
|
|
