Model Sources

This repo supports two model paths:

1. Hugging Face snapshots pulled into the local mere.run model store with mere.run model pull
2. Explicit local paths passed to commands with --model, --model-root, or equivalent command-specific options

The public repo no longer supports private model archives, R2 credentials, or a packaged central model host.

The canonical local model store is:

~/Library/Application Support/MereRun/models

Override that with MERERUN_MODELS_DIR or --models-root.

Canonical Managed Model IDs

This is the authoritative public catalog list. It is kept in sync with ManagedModelCatalog.allSpecs, and the test suite fails if the table drifts from the runtime catalog used by mere.run model list, mere.run model capabilities --all, and mere.run model pull.

Catalog category	Model ID
image	image-klein-nano
image	image-klein-max
image	image-klein-9b
image	image-klein-base
image	image-klein-base-9b
image	image-klein-shared
image	image-bonsai-binary
image	image-bonsai-ternary
image	image-zimage-nano
image	image-zimage-max
image	image-zimage-base
image	image-hidream-o1
image	image-hidream-o1-dev
image	image-krea2-raw
image	image-krea2-turbo
image	image-ideogram4-sdnq-uint4
text-chat	text-chat-mebot
text-chat	text-chat-psi-agent
text-chat	text-chat-gemma4
text-chat	text-chat-gemma4-turbo
text-chat	text-chat-gemma4-12b
text-chat	text-chat-gemma4-12b-4bit
vision-chat	vision-chat-gemma4-12b
text-chat	text-chat-gemma4-nano
text-chat	text-chat-gemma4-max
text-chat	text-chat-q36-nano
text-chat	text-chat-bonsai-27b-1bit
text-chat	text-chat-bonsai-27b-2bit
text-code	text-agent-ornith-9b
text-code	text-agent-ornith-35b-mlx
text-code	text-agent-qwen35-9b
text-code	text-code-north-mini
text-code	text-agent-ornith-35b
text-chat	text-chat-q36-nano-gguf
text-chat	text-agent-deepseek-v4-flash
text-chat	text-chat-lfm25-a1b-8bit
speech-tts	speech-tts-qwen3-nano
speech-tts	speech-tts-qwen3-customvoice
speech-asr	speech-asr-qwen3
speech-asr	speech-asr-parakeet
text-code	text-code-qwen3
text-embed	text-embed-qwen3-0.6b
text-anonymize	text-anonymize-privacy-filter
vision-ocr	vision-ocr-infinity-flash
vision-ocr	vision-ocr-infinity-pro
vision-ocr	vision-ocr-infinity-pro-int8
vision-ocr	vision-ocr-lighton
vision-segment	vision-segment-sam31
vision-ground	vision-ground-falcon-perception
vision-face	vision-face-buffalo-l
vision-geometry	vision-geometry-moge2-small
vision-depth	vision-depth-vda-small
vision-depth	vision-depth-vda-small-metric
vision-geometry	vision-geometry-da3-small
image-3d	image-3d-triposr
image-3d	image-3d-instantmesh-base
image-3d	image-3d-trellis2-4b
music	music-acestep
music	music-acestep-xl-turbo
music	music-acestep-xl-turbo-lm4b
music	music-magenta-rt2-small
music	music-magenta-rt2-base
music	music-muscriptor-small
music	music-muscriptor-medium
music	music-muscriptor-large
sfx	sfx-woosh-dflow
sfx	sfx-woosh-flow
sfx	sfx-woosh-clap
sfx	sfx-woosh-synchformer
sfx	sfx-woosh-vflow-8s
sfx	sfx-woosh-dvflow-8s
sfx	sfx-mmaudio-large-44k-v2
video	video-ltx-av
video	video-ltx23-av-mlx
video	video-ltx23-full-mlx
video	video-ltx23-a2vid-mlx
video	video-wan22-ti2v-5b-mlx
video	video-cosmos3-edge-mlx
video	video-scail2-14b-mlx
video	video-dreamx-world-5b-ar-mlx

Restricted model downloads

mere.run is free and open source, but model and component licenses remain the terms of their respective owners. mere.run does not bundle these weights, decide whether a user's intended use qualifies, or police use after install. For every new download with non-commercial, research-only, gated, revenue- limited, or custom acceptable-use terms, the user must review the listed terms and pass --accept-model-license:

mere.run model pull vision-face-buffalo-l --accept-model-license
mere.run model pull --all --accept-model-license

Without that flag, a single-model pull and its preflight are blocked; --all skips restricted models. Restricted models never auto-download from an inference command. The macOS app presents the same acknowledgement before a download and exposes the term links in its Models and Advanced views. Batch downloads through agent onboard --pull-recommended skip restricted entries without acknowledgement, while open-webui quickstart --pull validates all configured models before downloading any; both accept the same --accept-model-license flag.

Models	Upstream terms that require acknowledgement
image-klein-9b, image-klein-base-9b	FLUX Non-Commercial License v2.1; non-commercial, non-production use
image-zimage-nano	the quantized mirror declares the Tongyi Qianwen License; the official Z-Image-Turbo base is Apache 2.0
image-krea2-raw, image-krea2-turbo	Krea 2 Community License; commercial use is limited to entities below USD 1M trailing annual revenue, plus use/distribution conditions
image-ideogram4-sdnq-uint4	Ideogram Non-Commercial Model Agreement
text-chat-lfm25-a1b-8bit	LFM Open License v1.0; commercial use by entities at or above USD 10M annual revenue is excluded
vision-segment-sam31	Meta SAM License custom use, trade-control, attribution, and redistribution conditions
vision-face-buffalo-l	InsightFace pretrained weights; non-commercial research use
image-3d-trellis2-4b	the mounted DINOv3 encoder is gated under Meta's custom DINOv3 License
music-muscriptor-{small,medium,large}	CC BY-NC 4.0 model weights
sfx-woosh-*	CC BY-NC 4.0 Woosh or MMAudio Synchformer weights
sfx-mmaudio-large-44k-v2	CC BY-NC 4.0 MMAudio checkpoints plus Apple's research-only DFN5B encoder terms
video-ltx-av, video-ltx23-av-mlx, video-ltx23-full-mlx, video-ltx23-a2vid-mlx	LTX-2 Community License; entities at or above USD 10M annual revenue need a paid commercial license, plus acceptable-use conditions. The 2.3 MLX paths also install a hidden Gemma 3 text encoder under Google's Gemma Terms and Prohibited Use Policy.

The catalog pins every restricted download source to an immutable commit. New managed installs write those repository revisions, every applicable model/component license and URL, and the acknowledgement result into schema 3 of mererun_model.json. mere.run model info MODEL displays the same record. Pre-existing installs remain usable and are not retroactively treated as an acknowledgement.

Most catalog IDs have managed Hugging Face sources and can be installed with mere.run model pull. A small number of legacy/local catalog IDs remain so existing installs and explicit local paths keep working:

• image-klein-shared
• text-chat-mebot
• text-chat-psi-agent

image-klein-shared is an internal shared-component install shape, and the text-chat IDs listed here remain local-path-only until they have public Hugging Face sources.

text-agent-qwen35-9b is the low-memory setup-agent model. It uses the public Hugging Face source unsloth/Qwen3.5-9B-GGUF and selects Qwen3.5-9B-Q4_K_M.gguf.

text-code-north-mini installs the Unsloth GGUF quant of Cohere Labs' North Mini Code 1.0 at the pinned catalog revision. North Mini Code is a 30B total / 3B active coding MoE with a 256K advertised context window; mere.run uses the North-Mini-Code-1.0-UD-Q4_K_M.gguf file so the model runs through the same native Swift/llama.cpp text code path as the existing Qwen coder. It requires a llama.cpp runtime with cohere2moe architecture support.

text-agent-ornith-9b installs the public sahilchachra/ornith-1.0-9b-optiq-5bpw-mlx snapshot at the pinned catalog revision. Ornith is a DeepReinforce agentic coding model with Qwen3.5 text architecture metadata; mere.run treats this MLX OptiQ quant as a native Qwen-family runtime target for chat, api serve, and setup-agent experiments.

text-agent-ornith-35b-mlx is reserved for a locally converted Q4 MLX snapshot of deepreinforce-ai/Ornith-1.0-35B at the pinned catalog revision. It runs through the native Qwen-family runtime, but has no Hugging Face pull source yet; install a converted directory under the local model store before using it.

text-agent-ornith-35b installs DeepReinforce's public deepreinforce-ai/Ornith-1.0-35B-GGUF Q4_K_M file at the pinned catalog revision. It runs through the native Swift/llama.cpp text code path for larger Ornith coding-agent comparisons and uses a 32K runtime context by default to keep local evals predictable.

text-agent-deepseek-v4-flash is the preferred managed setup-agent tier on 96 GB+ Apple Silicon Macs. Smaller Qwen setup agents are lower-memory alternatives, not upgrades from DeepSeek V4 Flash.

text-chat-q36-nano uses the public mlx-community/Qwen3.6-35B-A3B-OptiQ-4bit snapshot. That Hugging Face repo includes an MTP head (mtp.safetensors) for OptiQ serving; mere.run loads that draft head when present, but only uses it for adaptive speculative decode when the effective prompt and context window are long enough. Short-context requests decode with the main chat weights.

text-chat-bonsai-27b-1bit and text-chat-bonsai-27b-2bit install Prism ML's public prism-ml/Bonsai-27B-mlx-1bit and prism-ml/Ternary-Bonsai-27B-mlx-2bit snapshots at exact catalog revisions. They are dense Qwen3.6 27B vision/reasoning models with packed affine 1-bit or 2-bit language weights and dense vision weights. The snapshots are approximately 5.13 GB and 8.52 GB respectively and advertise a 262,144-token context. mere.run uses the native Qwen-family text and vision runtime, native low-bit linear and embedding kernels, and the published thinking and sampling defaults. Both models are Apache-2.0 licensed; managed pulls retain upstream license and notice files.

text-chat-q36-nano-gguf installs the Unsloth Qwen3.6-35B-A3B-UD-Q4_K_M.gguf quant. It is the llama.cpp/GGUF companion to the Apple Silicon MLX text-chat-q36-nano path and is the default chat model for Linux CUDA hosts.

text-chat-gemma4-12b and vision-chat-gemma4-12b share Google's dense Gemma 4 12B-it checkpoint; the text id uses the native chat path, while the vision id enables OpenAI image content parts through api serve. Pulling either managed 12B id also pulls the companion google/gemma-4-12B-it-assistant MTP drafter. The native Swift runtime uses that assistant only for greedy decode-tail speculation after text, image, or audio prefill has produced target hidden state and shared KV; raw local model paths and sampled generations fall back to baseline decode. text-chat-gemma4 is the dense bf16 Gemma 4 31B alias and is gated for larger machines. On 32 GB Apple Silicon Macs, use text-chat-gemma4-turbo, which installs the MLX NVFP4 Gemma 4 26B-A4B-it MoE snapshot and runs through the native Swift Gemma runtime. On smaller supported machines, text-chat-gemma4-12b-4bit installs the checksum-pinned Sawfwair/gemma-4-12B-it-MLX-4bit snapshot as the compact managed Gemma 12B chat tier. Sawfwair's package is independently converted from Google's verified dense checkpoint, includes MERERUN_CONVERSION.json with source and emitted artifact hashes, and is published as the user-facing v1.0.0 release.

text-chat-lfm25-a1b-8bit uses the public LiquidAI/LFM2.5-8B-A1B-MLX-8bit snapshot at the pinned catalog revision. It is a text-only MLX 8-bit directory-root model with config.json, tokenizer.json, tokenizer_config.json, and sharded *.safetensors weights. mere.run runs it through the native Swift LFM2 runtime; no Python bridge is used.

Useful environment variables for that path:

• MERERUN_HUB_CACHE: override the native Hugging Face snapshot cache path

image-klein-9b installs the ungated mlx-community/FLUX.2-klein-9B mirror for larger distilled Klein generation and reference-image workflows. It is distinct from image-klein-base-9b, which pulls the undistilled Base 9B transformer plus shared 9B components for higher-capacity LoRA training and research workflows.

image-bonsai-binary and image-bonsai-ternary map to PrismML Apple Silicon Bonsai Image snapshots:

• prism-ml/bonsai-image-binary-4B-mlx-1bit
• prism-ml/bonsai-image-ternary-4B-mlx-2bit

The snapshot uses a FLUX.2 Klein transformer, but its component names are upstream-specific. Managed or local roots are expected to contain:

• manifest.json
• tokenizer/tokenizer_config.json
• text_encoder-mlx-4bit/config.json
• text_encoder-mlx-4bit/model.safetensors or model.safetensors.index.json
• transformer-packed-mflux/config.json
• transformer-packed-mflux/quantization_config.json
• transformer-packed-mflux/diffusion_pytorch_model.safetensors
• vae/config.json
• vae/diffusion_pytorch_model.safetensors
• scheduler/scheduler_config.json

The binary manifest records the transformer as 1-bit g128 Prism packed affine weights; the ternary manifest records 2-bit g128 MLX packed affine ternary weights. Both keep the text encoder in the upstream 4-bit MLX layout and run generation through the native Swift FLUX.2 Klein pipeline with four steps, CFG 1.0, and sigma shift 3.0 by default. The binary runtime path uses native packed 1-bit affine matmul kernels on Metal and Linux CUDA, with a dequantized MLX fallback for non-GPU or unsupported shapes.

vision-segment-sam31 packages the native SAM 3.1 segmentation and tracking runtime used by mere.run vision segment, mere.run vision track, and mere.run vision track-live. Managed or local SAM roots are expected to contain:

• config.json
• model.safetensors or model.safetensors.index.json
• tokenizer/tokenizer.json
• tokenizer/tokenizer_config.json

Tokenizer files are technically optional for geometry prompts, but managed installs include them because text prompts are the preferred segmentation path. The managed package mounts tokenizer assets from the SAM 3.1 mirror while the native MLX weights come from mlx-community/sam3.1-bf16.

The manifest for this package advertises both vision_segmentation and vision_tracking capabilities.

image-hidream-o1 and image-hidream-o1-dev map to the public HiDream O1 image checkpoints:

• HiDream-ai/HiDream-O1-Image
• HiDream-ai/HiDream-O1-Image-Dev

HiDream O1 uses a unified pixel-transformer root layout rather than a VAE/text-encoder component tree. Managed or local roots are expected to contain:

• config.json
• tokenizer_config.json
• tokenizer.json or vocab.json plus merges.txt
• preprocessor_config.json
• model.safetensors or model.safetensors.index.json

The native Swift runtime validates this layout, decodes the typed root configuration, prepares text/reference sample metadata, and runs generation through the downloaded Qwen3-VL decoder, vision tower, timestep embedder, patch embedder, generation-aware attention mask, and HiDream pixel head. Text-only generation, one-reference instruction editing, and multi-reference subject personalization share the same native path; reference modes additionally run Qwen3-VL vision preprocessing and replace chat-template image placeholders before denoising.

Runtime defaults come from the managed manifest:

• image-hidream-o1-dev: 28 steps, CFG 0.0, fixed flash FlowMatch schedule
• image-hidream-o1: 50 steps, CFG 5.0, shifted Flow UniPC schedule

Both checkpoints are large BF16 unified-transformer roots, about 33 GiB on disk each before filesystem compression effects. Expect high unified-memory pressure and prefer one-step smokes before full-quality 28/50 step runs.

image-krea2-raw and image-krea2-turbo map to Krea's public Krea 2 checkpoints:

• krea/Krea-2-Raw
• krea/Krea-2-Turbo

Managed or local roots are expected to contain the component Diffusers layout:

• model_index.json
• tokenizer/tokenizer_config.json
• tokenizer/tokenizer.json
• text_encoder/config.json
• text_encoder/model.safetensors
• transformer/config.json
• transformer/diffusion_pytorch_model.safetensors.index.json
• transformer/diffusion_pytorch_model-*.safetensors
• vae/config.json
• vae/diffusion_pytorch_model.safetensors
• scheduler/scheduler_config.json

Krea also publishes root-level raw.safetensors and turbo.safetensors transformer copies for the official codebase. Managed pulls intentionally exclude those files and pull the split transformer component instead, so the model store does not download the same large transformer payload twice.

The native Swift runtime follows the public Krea 2 sampler shape: Qwen3-VL text conditioning with the Krea system prefix, layer-selected hidden-state fusion, single-stream MMDiT denoising, 16-pixel image-token alignment, FlowMatch Euler steps, and Qwen Image VAE decoding. The wired public generation mode is text-to-image with optional LoRA adapters; reference images and image-to-image are not supported for this family yet. LoRA training uses image-krea2-raw; inference uses image-krea2-turbo.

Krea publishes sample LoRA adapters such as krea/Krea-2-LoRA-retroanime and krea/Krea-2-LoRA-kidsdrawing. Those adapters are trained on Raw and loaded on Turbo with Diffusers lora_A / lora_B keys. The native loader preserves Krea's img_in, txt_in, text_fusion, time_embed, time_mod_proj, transformer_blocks, and final_layer module names so those published adapters can be used as compatibility references.

Runtime defaults come from the managed manifest:

• image-krea2-raw: 52 steps, CFG 3.5, FlowMatch shift/mu 1.15
• image-krea2-turbo: 8 steps, CFG 0.0, FlowMatch shift/mu 1.15

The component install is about 36 GiB before filesystem compression effects. Expect high unified-memory pressure and prefer 96 GB+ Apple Silicon machines.

image-ideogram4-sdnq-uint4 maps to WaveCut's public SDNQ uint4 Ideogram 4 snapshot:

• WaveCut/ideogram-4-sdnq-uint4

Managed or local roots are expected to contain:

• model_index.json
• quantization_manifest.json
• tokenizer/tokenizer_config.json or tokenizer/tokenizer.json
• text_encoder/config.json
• transformer/config.json
• transformer/diffusion_pytorch_model.safetensors
• unconditional_transformer/config.json
• unconditional_transformer/diffusion_pytorch_model.safetensors
• vae/config.json
• vae/diffusion_pytorch_model.safetensors
• scheduler/scheduler_config.json

The managed manifest records SDNQ asymmetric uint4 weights and the separate positive and unconditional transformer branches used by Ideogram 4 guidance. The current native support can pull, inspect, validate, decode SDNQ uint4 linear, embedding, and Conv2d weights, build Qwen3-VL concatenated text features, pack Ideogram 4 text/image samples, run positive/unconditional CFG denoising, and decode PNG output through the Flux2-style VAE. Text-to-image image generate is wired; image-to-image, reference inputs, and LoRA are still unsupported for this family.

Hugging Face Cache

Hub snapshots use the shared cache location managed by the runtime. Override it when you want large models on an external disk:

export MERERUN_HUB_CACHE=/Volumes/Models/huggingface
swift run mere.run model pull image-zimage-nano --accept-model-license

Model pulls are resumable at the file level through the Hugging Face snapshot cache. The CLI writes a managed-model symlink from the mere.run model store to the prepared snapshot when needed.

Hardware Support Checks

Managed pulls are gated by the local capability catalog before any download. The check uses supported local runtimes plus memory thresholds for each model family, then blocks models that are unlikely to run reliably on the current machine.

Inspect the local recommendation first:

swift run mere.run model capabilities
swift run mere.run model capabilities --all

If you are intentionally testing an unsupported setup, pass --allow-unsupported to mere.run model pull.

Model Store Behavior

The CLI resolves models in this order:

1. --models-root process override
2. MERERUN_MODELS_DIR
3. persisted local model-store setting
4. default ~/Library/Application Support/MereRun/models

Examples:

# Pull into the default model store
swift run mere.run model pull image-zimage-nano --accept-model-license

# Pull into a custom SSD-backed store
MERERUN_MODELS_DIR=/Volumes/Models swift run mere.run model pull text-chat-q36-nano
MERERUN_MODELS_DIR=/Volumes/Models swift run mere.run model pull text-chat-lfm25-a1b-8bit --accept-model-license

# Inspect what is currently installed
swift run mere.run status
swift run mere.run model list
swift run mere.run model info image-klein-max

Music, SFX, And Video Layouts

Some retained surfaces have more structure than a flat model root.

music-acestep, music-acestep-xl-turbo, and music-acestep-xl-turbo-lm4b

The top-level model roots are:

.../models/music-acestep
.../models/music-acestep-xl-turbo
.../models/music-acestep-xl-turbo-lm4b

Those roots may contain:

• acestep-v15-turbo/
• acestep-v15-xl-turbo/
• acestep-5Hz-lm-1.7B/ or another supported LM subdirectory
• acestep-5Hz-lm-4B/
• Qwen3-Embedding-0.6B/
• vae/

Older local installs that still use music-acestep-v15-turbo/ remain supported.

music-acestep-xl-turbo pulls the ACE-Step 1.5 XL turbo DiT into acestep-v15-xl-turbo/ and reuses the base ACE-Step 1.5 VAE and Qwen3 text encoder components. music-acestep-xl-turbo-lm4b adds the optional acestep-5Hz-lm-4B/ 5 Hz LM. The default ACE-Step LM component discovery continues to prefer the smaller acestep-5Hz-lm-1.7B/ when both LM directories are present; pass --lm-subdirectory acestep-5Hz-lm-4B to force the 4B LM.

mere.run music generate and mere.run music analyze auto-discover these layouts unless you override the root with --checkpoints-root or MERERUN_MUSIC_ACESTEP_ROOT.

music-magenta-rt2-small and music-magenta-rt2-base

Magenta RT2 models use exported runtime assets from google/magenta-realtime-2 at revision 010aa0dcb0dfd27b24f0ad07b4dad63e8f9521cc. The managed pull keeps only the files needed by the native runtime:

.../models/music-magenta-rt2-small
├── models/mrt2_small/mrt2_small.mlxfn
├── models/mrt2_small/mrt2_small_state.safetensors
├── resources/musiccoca/
└── resources/spectrostream/

The base model uses models/mrt2_base/ with matching mrt2_base filenames. Raw checkpoints/*.safetensors files are not a complete mere.run layout.

mere.run music generate --model music-magenta-rt2-small renders an offline WAV. mere.run music realtime --model music-magenta-rt2-small plays on the default macOS audio device and can capture to WAV with --output.

music-muscriptor-small, music-muscriptor-medium, and music-muscriptor-large

MuScriptor checkpoints come from the gated MuScriptor/muscriptor-{size} Hugging Face repositories. Each managed root contains config.json and model.safetensors. The weights are CC BY-NC 4.0 and require accepting the upstream access terms before mere.run model pull can download them.

mere.run music transcribe decodes input audio to mono 16 kHz, runs the exact published five-second HTK mel frontend and native MLX transformer, then writes a format-1 MIDI file with one track per detected instrument. JSON and JSONL event output are also available.

sfx-woosh-dflow

The Woosh DFlow model root is:

.../models/sfx-woosh-dflow
└── checkpoints/
    ├── Woosh-DFlow/
    ├── Woosh-AE/
    └── TextConditionerA/
        └── tokenizer/

The managed pull uses the AEmotionStudio/woosh-models Hugging Face mirror for Sony Research Woosh v1.0.0 weights and mounts FacebookAI/roberta-large tokenizer files under checkpoints/TextConditionerA/tokenizer/. The native runtime exposes the text-to-SFX distilled DFlow path through mere.run sfx generate.

sfx-woosh-flow

The Woosh original Flow model root is:

.../models/sfx-woosh-flow
└── checkpoints/
    ├── Woosh-Flow/
    ├── Woosh-AE/
    └── TextConditionerA/
        └── tokenizer/

The managed pull uses the same mirror and tokenizer mount as sfx-woosh-dflow. The native runtime exposes the original text-to-SFX Flow checkpoint through mere.run sfx generate --model sfx-woosh-flow; it generally needs more denoise steps than the distilled model.

Woosh CLAP and V2A

sfx-woosh-clap installs checkpoints/Woosh-CLAP/ plus the mounted RoBERTa tokenizer for native text/audio scoring through mere.run sfx clap score.

sfx-woosh-dvflow-8s and sfx-woosh-vflow-8s install the distilled and original video-to-audio checkpoint stacks from AEmotionStudio/woosh-models. Both include checkpoints/Woosh-AE/ and checkpoints/TextConditionerV/.

sfx-woosh-synchformer installs the companion mmaudio_synchformer_fp16.safetensors visual extractor from Kijai/MMAudio_safetensors. mere.run sfx video generate uses it when the input is a raw video file; .npy inputs can still provide precomputed Synchformer synch_out features directly.

sfx-mmaudio-large-44k-v2

The native MMAudio install combines pinned public artifacts into one managed root:

.../models/sfx-mmaudio-large-44k-v2
├── mmaudio_large_44k_v2_fp16.safetensors
├── apple_DFN5B-CLIP-ViT-H-14-384_fp16.safetensors
├── mmaudio_synchformer_fp16.safetensors
├── mmaudio_vae_44k_fp16.safetensors
├── clip/tokenizer.json
└── bigvgan/
    ├── config.json
    └── bigvgan_generator.pt

The MMAudio, CLIP, Synchformer, and VAE safetensors come from the pinned Kijai/MMAudio_safetensors snapshot. CLIP tokenizer/config files are mounted from Apple's pinned DFN5B CLIP repository. BigVGAN-v2 config and generator weights are mounted from NVIDIA's pinned 44.1 kHz repository. The Swift runtime loads the official BigVGAN PyTorch state dictionary with a restricted parser; it does not execute Python or arbitrary pickle globals.

The hkchengrex/MMAudio architecture source is MIT-licensed. The released MMAudio checkpoints are separately CC-BY-NC-4.0 and therefore non-commercial. Apple's mounted DFN5B CLIP model is separately restricted to research purposes under the Apple Machine Learning Research Model License Agreement. NVIDIA's BigVGAN-v2 source and model are MIT-licensed. The managed install retains the exact Apple and NVIDIA license files beside those components. Review all model terms before downloading or using generated output.

video-ltx-av

The unified AV model root is:

.../models/video-ltx-av

mere.run video generate --quality draft --model video-ltx-av is the faster video-only draft path. The legacy --variant selector remains available for older scripts using this root. MERERUN_VIDEO_LTX_MODEL_ROOT can still point at this layout explicitly.

video-ltx23-av-mlx

The LTX 2.3 MLX split model root is:

.../models/video-ltx23-av-mlx

It pulls the distilled split checkpoint from dgrauet/ltx-2.3-mlx, including split_model.json, connector weights, separate video VAE/audio VAE/vocoder files, and the LTX 2.3 upscalers. mere.run model pull video-ltx23-av-mlx also installs the hidden text-encoder-ltx-gemma3-12b-4bit companion used for Gemma 3 prompt conditioning. Set MERERUN_VIDEO_LTX_TEXT_ENCODER_ROOT only when pointing at an external mlx-community/gemma-3-12b-it-4bit checkout.

The native Swift runtime uses this standalone distilled transformer for the fast --quality draft lane. The default --output-mode video-only route retains the checkpoint's joint AV denoising tokens but does not load or decode the audio VAE/vocoder, and its MP4 contains no audio stream. It can also emit synchronized AV with --output-mode audio-video, but the canonical final quality path uses video-ltx23-full-mlx. The Unsloth LTX-2.3-GGUF checkpoint family is a separate quantized GGUF lane and is not loaded by the native MLX video runtime.

video-ltx23-full-mlx

The full LTX 2.3 quality root is:

.../models/video-ltx23-full-mlx

It pulls the full/dev transformer, the official rank-384 distilled LoRA, connector, audio VAE, BWE vocoder, video VAE encoder/decoder, and x2 spatial upscaler from the same immutable dgrauet/ltx-2.3-mlx revision. It does not duplicate the standalone distilled transformer or pull unrelated x1.5 and temporal upscalers. Hugging Face cache objects are shared with video-ltx23-av-mlx when both models are installed. The hidden Gemma 3 companion is shared as well.

The same bundle drives all official two-stage final-quality contracts. The default --quality final --output-mode video-only route runs the full video pipeline without requiring audio in the deliverable. With --output-mode audio-video, unified AV jointly denoises guided video and audio latents in stage one, then refines both after LoRA fusion. A2Vid encodes source audio and freezes those audio latents through both stages; the original decoded source segment—not VAE-decoded audio—is muxed into the MP4.

video-ltx23-a2vid-mlx

This deprecated compatibility ID preserves existing A2Vid installs and scripts:

.../models/video-ltx23-a2vid-mlx

Its legacy narrow manifest may omit the vocoder, so it can run final-quality video-only and source-audio A2Vid but not generated-audio output. Requests for either the legacy ID or the new full ID resolve to an already-installed compatible root when possible. New pulls should use video-ltx23-full-mlx.

video-wan22-ti2v-5b-mlx

The native Wan2.2 TI2V-5B model root is:

.../models/video-wan22-ti2v-5b-mlx

mere.run model pull video-wan22-ti2v-5b-mlx installs a pinned MLX conversion of Wan-AI/Wan2.2-TI2V-5B. The root contains the single 5B transformer, local UMT5 tokenizer and encoder, and the float32 48-channel Wan2.2 VAE required for image conditioning. The managed snapshot is pinned by revision; inference does not fetch tokenizer or model components at runtime.

The core runtime also exposes Wan2WorldSession for long-lived world generation. One session retains the models, prompt cache, and terminal-frame latent across transitions; callers receive MP4/PNG state artifacts and opaque state IDs rather than mutable MLX tensors. Camera controls are represented as XYZ translation and rotation so a DreamX-derived causal camera conditioner can replace the current text-plus-first-frame mode without changing the session schema.

video-scail2-14b-mlx

The native SCAIL-2 model root is:

.../models/video-scail2-14b-mlx

The pinned Sawfwair/SCAIL-2-14B-MLX release package contains sharded BF16 transformer and UMT5 weights, FP16 OpenCLIP weights, the Wan 2.1 VAE, tokenizer, configuration, MIT license, model card, and a conversion receipt with immutable source and artifact hashes. The package is approximately 43 GiB.

The mere.run repository contains only the native Swift/MLX runtime. It does not ship a checkpoint converter or an upstream Python reference implementation. Install the immutable managed snapshot with:

mere.run model pull video-scail2-14b-mlx

An explicitly prepared compatible root can still be supplied with --model-root.

The optional scail2-lightx2v-4step adapter is not part of that model package or this repository. mere.run adapter pull downloads only wan2.1_i2v_lora_rank64_lightx2v_4step.safetensors from lightx2v/Wan2.1-Distill-Loras at immutable revision 27ae38da91014b947dd39cc3fa78b97cd7b386dd, verifies 739,472,104 bytes and SHA-256 8833bd4fd7c8eabebf0bc8ee5cfaf47f4f310ce116928a02c1adf8941dd4b0f1, and stores it outside git under the managed adapter directory. The upstream model card declares Apache-2.0. The Wan 2.2 adapters are deliberately rejected for SCAIL-2 because they target the incompatible Wan 2.2 MoE base.

video-cosmos3-edge-mlx

The native Cosmos3-Edge root is the complete official nvidia/Cosmos3-Edge snapshot pinned at revision 6f58f6b4c91288838e60b6bcb2cc45d997e961de:

.../models/video-cosmos3-edge-mlx

The approximately 9.2 GB snapshot includes the mixed understanding/generation transformer, Wan VAE, scheduler, generation tokenizer, reasoner tokenizer/configuration, packed SigLIP2 vision encoder, and multimodal projector. The Swift/MLX runtime loads these official safetensors directly; it does not invoke Python, PyTorch, or Diffusers during inference.

mere.run model pull video-cosmos3-edge-mlx --accept-model-license
mere.run guide video-cosmos3

The checkpoint is governed by NVIDIA Open Model Development and Use License 1.1 (OpenMDW-1.1). The managed catalog exposes the pinned license URL, requires explicit acceptance, and disables runtime auto-download.

Numerical parity fixtures are generated against NVIDIA's Cosmos framework commit ed8287fd7477113f8ac4f6b84290514d55cf0cdc; the VAE/scheduler reference is Diffusers v0.39.0 commit a3608b512ed7248499a44c61d954965ed9bdae4d.

video-dreamx-world-5b-ar-mlx

The native DreamX-World autoregressive checkpoint root is:

.../models/video-dreamx-world-5b-ar-mlx

The public CLI does not convert this local-only checkpoint. Supply a previously converted GD-ML/DreamX-World-5B root at the managed path above or pass its directory to mere.run world serve --model. The runtime pairs it with video-wan22-ti2v-5b-mlx for tokenizer, text encoder, and VAE resources. The checkpoint provides learned camera conditioning, block-causal attention, persistent attention caches, and autoregressive forcing for long-lived local world sessions. It is never downloaded or converted automatically at runtime.

See Persistent World Runtime for the server and request lifecycle.