Open-weight model
MiMo-V2.6-Pro-RL-ARVQ-hybrid
by Jarrel Seah jarrelscy/MiMo-V2.6-Pro-RL-ARVQ-hybrid
MiMo-V2.6-Pro-RL-ARVQ-hybrid is an open-weight model from Jarrel Seah, released under MIT License. It has 119B parameters and a 1,048,576-token context. At 16-bit it needs about 285.6 GB of GPU memory, which fits on 1x MI355X from $2.59 an hour; at 4-bit, 71.4 GB on 1x MI300X from $1.85, at the lowest prices in the SAVRN Index.
Complete uploaded 5% hot checkpoint: 1325 of 26496 routed experts use NVFP4. Remaining experts use per-expert ARVQ FP4 books and FP16 block scales.
Parameters119B
Context1,048,576
Weights322.7 GB
Licensemit
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve MiMo-V2.6-Pro-RL-ARVQ-hybrid (119B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 238.0 GB | 285.6 GB | 1x MI355X (288 GB) Vultr |
$2.59 | 2x MI300X $3.70 · 2x MI325X $4.00 |
| 8-bit | 119.0 GB | 142.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x MI325X $2.00 · 1x MI355X $2.59 |
| 4-bit | 59.5 GB | 71.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 1, 2026.
MiMo-V2.6-Pro-RL-ARVQ-hybrid on every accelerator the SAVRN Index prices, at every precision
Model Card
By Jarrel Seah, published under mit, revision 67d6be01a93e.
Complete uploaded 5% hot checkpoint: 1325 of 26496 routed experts use NVFP4. Remaining experts use per-expert ARVQ FP4 books and FP16 block scales. Global allocation ranks routing-weighted output-error benefit measured on 65536 training-only calibration tokens. All 69 layers have prepared weights. Sequential PV is continuing through the remaining layers. Layers 1-20 reuse the earlier accepted all-cold candidates (layer 9 retained its initial fit). Their cold weight tensor payloads were verified identical to those underlying the cached inputs for layer 21. Layers 21 and 22 have completed PV; layers 23-69 follow sequentially, freezing the selected NVFP4 hot experts. Each accepted layer…
Read Jarrel Seah's full model card
MiMo-V2.6-Pro-RL ARVQ / NVFP4 hybrid
Complete uploaded 5% hot checkpoint: 1325 of 26496 routed experts use NVFP4. Remaining experts use per-expert ARVQ FP4 books and FP16 block scales. Global allocation ranks routing-weighted output-error benefit measured on 65536 training-only calibration tokens. All 69 layers have prepared weights.
Sequential PV is continuing through the remaining layers. Layers 1-20 reuse the earlier accepted all-cold candidates (layer 9 retained its initial fit). Their cold weight tensor payloads were verified identical to those underlying the cached inputs for layer 21. Layers 21 and 22 have completed PV; layers 23-69 follow sequentially, freezing the selected NVFP4 hot experts. Each accepted layer replaces its initial fit on HF as an atomic weights/roster/config commit. See pv_progress.json for verified upload status. Unfinished layers retain their already-uploaded initial fits. This is not yet a fully PV-completed or quality-qualified checkpoint.
All backbone, MTP, vision, audio, audio-tokenizer and DFlash weights are retained. Calibration is text-only with arithmetic emulation. Full-model quality evaluation is queued after remaining PV; SM120 serving and 1M-context memory fit remain unqualified. upload_verification.json identifies the snapshot whose hashes were verified; subsequent PV commits have their own per-layer hash checks.
Configuration
- Architecture
- MiMoV2ForCausalLM
- Context length (tokens)
- 1,048,576
- Layers
- 70
- Hidden size
- 6,144
- Feed-forward size
- 16,384
- Attention heads
- 128
- Key/value heads
- 8
- Head dimension
- 192
- Vocabulary size
- 152,576
- Routed experts
- 384
- Experts active per token
- 8
- Sliding window (tokens)
- 128
- RoPE base
- 10,000,000
- Model type
- mimo_v2
- Quantization
- nvfp4_arvq_hybrid
Identity and Version
- Repository
- jarrelscy/MiMo-V2.6-Pro-RL-ARVQ-hybrid
- Publisher
- Jarrel Seah
- Task
- Not stated by the source
- Modality
- Other
- Library
- vllm
- Parameters
- 119B parameters
- Languages
- Not stated by the source
- Revision
- 67d6be01a93e0c64e0ff1ade26e9978c4018ba45
- First published
- 2026-09-22
- Last updated
- 2026-09-23
Files and Weights
441 files, 322.7 GB in total. The weights are 213 files totalling 322.7 GB in pt, safetensors.
Weights213 files · 322.7 GB
Configuration217 files · 3.1 MB
Tokenizer5 files · 15.9 MB
Documentation1 file · 1.5 KB
Other4 files · 3.5 MB
Repository1 file · 1.7 KB
Every file
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| arvq-layer-001-down.safetensors | Weights | 1.3 GB | a7cb7ebb8653 |
| arvq-layer-001-gateup.safetensors | Weights | 2.6 GB | 5693621b1834 |
| arvq-layer-002-down.safetensors | Weights | 1.3 GB | 6b6b17fdb1f0 |
| arvq-layer-002-gateup.safetensors | Weights | 2.6 GB | fd5262bf4643 |
| arvq-layer-003-down.safetensors | Weights | 1.3 GB | e8f710a52ec5 |
| arvq-layer-003-gateup.safetensors | Weights | 2.6 GB | 4b4f5112e3c9 |
| arvq-layer-004-down.safetensors | Weights | 1.3 GB | a2b7c69a5ef4 |
| arvq-layer-004-gateup.safetensors | Weights | 2.6 GB | 6ed97e6aaf39 |
| arvq-layer-005-down.safetensors | Weights | 1.3 GB | 6910dff93e09 |
| arvq-layer-005-gateup.safetensors | Weights | 2.6 GB | 5f0d74eb9e85 |
| arvq-layer-006-down.safetensors | Weights | 1.3 GB | 9625a50634b4 |
| arvq-layer-006-gateup.safetensors | Weights | 2.6 GB | beac26f68b08 |
| arvq-layer-007-down.safetensors | Weights | 1.3 GB | 288f964cc89f |
| arvq-layer-007-gateup.safetensors | Weights | 2.6 GB | e8e0e5f88b63 |
| arvq-layer-008-down.safetensors | Weights | 1.3 GB | ee2d4b604821 |
| arvq-layer-008-gateup.safetensors | Weights | 2.6 GB | ac42841bfe7e |
| arvq-layer-009-down.safetensors | Weights | 1.3 GB | 58622b1f6a84 |
| arvq-layer-009-gateup.safetensors | Weights | 2.6 GB | 9bd78e0093ff |
| arvq-layer-010-down.safetensors | Weights | 1.3 GB | 8280cf3c6a50 |
| arvq-layer-010-gateup.safetensors | Weights | 2.6 GB | e0438ceb7e0a |
| arvq-layer-011-down.safetensors | Weights | 1.3 GB | 98b01fd0d1af |
| arvq-layer-011-gateup.safetensors | Weights | 2.6 GB | 82eb2cbe751a |
| arvq-layer-012-down.safetensors | Weights | 1.3 GB | 379a87fdb18e |
| arvq-layer-012-gateup.safetensors | Weights | 2.6 GB | 65f734c09ed3 |
| arvq-layer-013-down.safetensors | Weights | 1.3 GB | 34937e89a7a1 |
| arvq-layer-013-gateup.safetensors | Weights | 2.6 GB | 7d38708a866b |
| arvq-layer-014-down.safetensors | Weights | 1.3 GB | 5a6bb7659d54 |
| arvq-layer-014-gateup.safetensors | Weights | 2.6 GB | 0e2ee947d69d |
| arvq-layer-015-down.safetensors | Weights | 1.3 GB | ada24e6b7b36 |
| arvq-layer-015-gateup.safetensors | Weights | 2.6 GB | 491d8c2714e2 |
| arvq-layer-016-down.safetensors | Weights | 1.3 GB | 4bb81ca3f7e3 |
| arvq-layer-016-gateup.safetensors | Weights | 2.6 GB | 6ddefefd7635 |
| arvq-layer-017-down.safetensors | Weights | 1.3 GB | 4a1e4cade6eb |
| arvq-layer-017-gateup.safetensors | Weights | 2.6 GB | 79a927bb7ac2 |
| arvq-layer-018-down.safetensors | Weights | 1.3 GB | a648ecd9ceb4 |
| arvq-layer-018-gateup.safetensors | Weights | 2.6 GB | 60828217f5f7 |
| arvq-layer-019-down.safetensors | Weights | 1.3 GB | 3607da2e0363 |
| arvq-layer-019-gateup.safetensors | Weights | 2.6 GB | 92538b002b7d |
| arvq-layer-020-down.safetensors | Weights | 1.3 GB | 261fed9a49c9 |
| arvq-layer-020-gateup.safetensors | Weights | 2.6 GB | 1b4e0c748686 |
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| arvq-layer-021-gateup.safetensors | Weights | 2.6 GB | e008fc57d35e |
| arvq-layer-022-down.safetensors | Weights | 1.3 GB | b5784d931378 |
| arvq-layer-022-gateup.safetensors | Weights | 2.6 GB | 367946f65b6f |
| arvq-layer-023-down.safetensors | Weights | 1.3 GB | 27d5819cdb71 |
| arvq-layer-023-gateup.safetensors | Weights | 2.6 GB | 81a077e678fb |
| arvq-layer-024-down.safetensors | Weights | 1.3 GB | b05f39d5766a |
| arvq-layer-024-gateup.safetensors | Weights | 2.6 GB | abce36759d4b |
| arvq-layer-025-down.safetensors | Weights | 1.3 GB | d6fedf1d2f77 |
| arvq-layer-025-gateup.safetensors | Weights | 2.6 GB | 0ac1c69cef94 |
| arvq-layer-026-down.safetensors | Weights | 1.3 GB | d14044108e79 |
| arvq-layer-026-gateup.safetensors | Weights | 2.6 GB | aac4a9f77598 |
| arvq-layer-027-down.safetensors | Weights | 1.3 GB | bfec6ecab5ae |
| arvq-layer-027-gateup.safetensors | Weights | 2.6 GB | bc6839c67a67 |
| arvq-layer-028-down.safetensors | Weights | 1.3 GB | 623b492b7ed3 |
| arvq-layer-028-gateup.safetensors | Weights | 2.6 GB | 6cbba5f6017a |
| arvq-layer-029-down.safetensors | Weights | 1.3 GB | 8aa8d0f0a790 |
| arvq-layer-029-gateup.safetensors | Weights | 2.6 GB | 59d501b4b0c1 |
| arvq-layer-030-down.safetensors | Weights | 1.3 GB | badc83f71043 |
| arvq-layer-030-gateup.safetensors | Weights | 2.6 GB | 6e5f6bb4beef |
| arvq-layer-031-down.safetensors | Weights | 1.3 GB | 6abe127c65b5 |
| arvq-layer-031-gateup.safetensors | Weights | 2.6 GB | 50c553b127e6 |
| arvq-layer-032-down.safetensors | Weights | 1.3 GB | a03465d1cdd3 |
| arvq-layer-032-gateup.safetensors | Weights | 2.6 GB | f936552b9e74 |
| arvq-layer-033-down.safetensors | Weights | 1.3 GB | afcd03a5d834 |
| arvq-layer-033-gateup.safetensors | Weights | 2.6 GB | 1eb7ce97b68c |
| arvq-layer-034-down.safetensors | Weights | 1.3 GB | 6edc97246f0c |
| arvq-layer-034-gateup.safetensors | Weights | 2.6 GB | 93b430994d77 |
| arvq-layer-035-down.safetensors | Weights | 1.3 GB | cdbe584dcd73 |
| arvq-layer-035-gateup.safetensors | Weights | 2.6 GB | 0e3685afacb0 |
| arvq-layer-036-down.safetensors | Weights | 1.3 GB | 6679689c2d27 |
| arvq-layer-036-gateup.safetensors | Weights | 2.6 GB | c8dcdb88964b |
| arvq-layer-037-down.safetensors | Weights | 1.3 GB | b3ef4a1fa586 |
| arvq-layer-037-gateup.safetensors | Weights | 2.6 GB | 141867456199 |
| arvq-layer-038-down.safetensors | Weights | 1.3 GB | 904e5d7b5449 |
| arvq-layer-038-gateup.safetensors | Weights | 2.6 GB | 903cb018f624 |
| arvq-layer-039-down.safetensors | Weights | 1.3 GB | 30dc61b75dfb |
| arvq-layer-039-gateup.safetensors | Weights | 2.6 GB | 038d0dcbe875 |
| arvq-layer-040-down.safetensors | Weights | 1.3 GB | 65dcbcf7aaf7 |
| arvq-layer-040-gateup.safetensors | Weights | 2.6 GB | 3f20afee61e6 |
| arvq-layer-041-down.safetensors | Weights | 1.3 GB | a62a13276dbe |
| arvq-layer-041-gateup.safetensors | Weights | 2.6 GB | dc13b8130159 |
| arvq-layer-042-down.safetensors | Weights | 1.3 GB | be6f27c08ccf |
| arvq-layer-042-gateup.safetensors | Weights | 2.6 GB | 7c139d353dc4 |
| arvq-layer-043-down.safetensors | Weights | 1.3 GB | 62cc4c7ad64e |
| arvq-layer-043-gateup.safetensors | Weights | 2.6 GB | 4d8864a84291 |
| arvq-layer-044-down.safetensors | Weights | 1.3 GB | 3353821e4daa |
| arvq-layer-044-gateup.safetensors | Weights | 2.6 GB | 511e35d00d4d |
| arvq-layer-045-down.safetensors | Weights | 1.3 GB | 1d0f549a66ee |
| arvq-layer-045-gateup.safetensors | Weights | 2.6 GB | 74b133c736b9 |
| arvq-layer-046-down.safetensors | Weights | 1.3 GB | 469347cdbbd8 |
| arvq-layer-046-gateup.safetensors | Weights | 2.6 GB | e3e3f567d858 |
| arvq-layer-047-down.safetensors | Weights | 1.3 GB | 4d142ba23c28 |
| arvq-layer-047-gateup.safetensors | Weights | 2.5 GB | 29bd44172240 |
| arvq-layer-048-down.safetensors | Weights | 1.3 GB | 824afe42243e |
| arvq-layer-048-gateup.safetensors | Weights | 2.5 GB | 2655b3ac1064 |
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| arvq-layer-049-gateup.safetensors | Weights | 2.5 GB | 44a06ee90cd5 |
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| arvq-layer-050-gateup.safetensors | Weights | 2.5 GB | 59a186cf0cba |
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| arvq-layer-051-gateup.safetensors | Weights | 2.5 GB | 5fb4428e0df9 |
| arvq-layer-052-down.safetensors | Weights | 1.3 GB | 1c4a12006e33 |
| arvq-layer-052-gateup.safetensors | Weights | 2.5 GB | c9c2ade8cc14 |
| arvq-layer-053-down.safetensors | Weights | 1.2 GB | 8d843353a1d7 |
| arvq-layer-053-gateup.safetensors | Weights | 2.5 GB | 0cebc9fd3591 |
| arvq-layer-054-down.safetensors | Weights | 1.2 GB | c7159033f92a |
| arvq-layer-054-gateup.safetensors | Weights | 2.5 GB | f70541b0a7bc |
| arvq-layer-055-down.safetensors | Weights | 1.2 GB | 97f39190e693 |
| arvq-layer-055-gateup.safetensors | Weights | 2.4 GB | 2c5db39e7dd8 |
| arvq-layer-056-down.safetensors | Weights | 1.2 GB | 76934f8e349b |
| arvq-layer-056-gateup.safetensors | Weights | 2.3 GB | c0d2a721dcef |
| arvq-layer-057-down.safetensors | Weights | 1.2 GB | 9b24d29afbbe |
| arvq-layer-057-gateup.safetensors | Weights | 2.3 GB | 17afdca6ae63 |
| arvq-layer-058-down.safetensors | Weights | 1.1 GB | a7725e5d11ea |
| arvq-layer-058-gateup.safetensors | Weights | 2.2 GB | 916be780cbc4 |
| arvq-layer-059-down.safetensors | Weights | 1.1 GB | 8cd8f2bf2418 |
| arvq-layer-059-gateup.safetensors | Weights | 2.1 GB | ecc89ba7187e |
| arvq-layer-060-down.safetensors | Weights | 1.0 GB | 611295a9959f |
| arvq-layer-060-gateup.safetensors | Weights | 2.1 GB | f950c33da5af |
| arvq-layer-061-down.safetensors | Weights | 1.1 GB | f6b1ea71e260 |
| arvq-layer-061-gateup.safetensors | Weights | 2.1 GB | a279bae382a9 |
| arvq-layer-062-down.safetensors | Weights | 1.0 GB | a6cf061544c8 |
| arvq-layer-062-gateup.safetensors | Weights | 2.0 GB | 5fd7f7592dc0 |
| arvq-layer-063-down.safetensors | Weights | 1.0 GB | ca0686f797e5 |
| arvq-layer-063-gateup.safetensors | Weights | 2.1 GB | d1efdfd173aa |
| arvq-layer-064-down.safetensors | Weights | 973.2 MB | 736ce2d86dc9 |
| arvq-layer-064-gateup.safetensors | Weights | 1.9 GB | 195e06fd6259 |
| arvq-layer-065-down.safetensors | Weights | 936.4 MB | 8b2841783815 |
| arvq-layer-065-gateup.safetensors | Weights | 1.9 GB | c5bbbf8cdf98 |
| arvq-layer-066-down.safetensors | Weights | 993.3 MB | e01a93e679b5 |
| arvq-layer-066-gateup.safetensors | Weights | 2.0 GB | 485ad3090858 |
| arvq-layer-067-down.safetensors | Weights | 899.6 MB | 8fd165de7ef0 |
| arvq-layer-067-gateup.safetensors | Weights | 1.8 GB | 7e688b8c704b |
| arvq-layer-068-down.safetensors | Weights | 812.7 MB | 8ebc2500de32 |
| arvq-layer-068-gateup.safetensors | Weights | 1.6 GB | de75bcb1419d |
| arvq-layer-069-down.safetensors | Weights | 642.1 MB | 9d28f23955b2 |
| arvq-layer-069-gateup.safetensors | Weights | 1.3 GB | b948f6e111c5 |
| audio_tokenizer/model.safetensors | Weights | 1.9 GB | 077033345d80 |
| backbone-000.safetensors | Weights | 2.0 GB | 4f5be09d843b |
| backbone-001.safetensors | Weights | 30.2 GB | d857fa0d0b69 |
| backbone-002.safetensors | Weights | 2.5 GB | eba334c8613d |
| dflash/dflash_draft_model.safetensors | Weights | 5.5 GB | 39208e3dd452 |
| dflash/mask_embedding.pt | Weights | 14.0 KB | 436ad0885387 |
| roster-layer-001.safetensors | Weights | 1.1 KB | d278bbd42534 |
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| roster-layer-055.safetensors | Weights | 615.8 MB | 13b4368048d3 |
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| roster-layer-068.safetensors | Weights | 3.0 GB | 837223040911 |
| roster-layer-069.safetensors | Weights | 4.1 GB | 2dfb310bbdf3 |
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| README.md | Documentation | 1.5 KB | — |
| MiMo_V2_6_technical_report.pdf | Other | 3.0 MB | fb81e6e08380 |
| assets/architecture.png | Other | 405.4 KB | d288768e1771 |
| audio_tokenizer/chat_template.jinja | Other | 5.6 KB | — |
| chat_template.jinja | Other | 3.9 KB | — |
| .gitattributes | Repository | 1.7 KB | — |
| audio_tokenizer/tokenizer_config.json | Tokenizer | 6.1 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 11.4 MB | ff15eb925890 |
| tokenizer_config.json | Tokenizer | 12.2 KB | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 322.7 GB
Download from Jarrel Seah
Released by Jarrel Seah through its official repository on Hugging Face. Read the license.
Built From
- Derived from XiaomiMiMo/MiMo-V2.6-Pro-RL
- Quantized from XiaomiMiMo/MiMo-V2.6-Pro-RL
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 322.7 GB |
| 16-bit | 238.0 GB |
| 8-bit | 119.0 GB |
| 4-bit | 59.5 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Quantized fromkeys-MiMo-V2.6-Pro-RL-Jarrelscy-ARVQ-Abliterated
- Derived fromkeys-MiMo-V2.6-Pro-RL-Jarrelscy-ARVQ-Abliterated
Questions About MiMo-V2.6-Pro-RL-ARVQ-hybrid
How much GPU memory does MiMo-V2.6-Pro-RL-ARVQ-hybrid need?
About 285.6 GB at 16-bit and 71.4 GB at 4-bit: the weights (119B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run MiMo-V2.6-Pro-RL-ARVQ-hybrid on?
At 16-bit, 1x MI355X from $2.59 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use MiMo-V2.6-Pro-RL-ARVQ-hybrid commercially?
Yes. MiMo-V2.6-Pro-RL-ARVQ-hybrid is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
What is MiMo-V2.6-Pro-RL-ARVQ-hybrid's context length?
1,048,576 tokens, from the maximum position embeddings in its published configuration.