Text Generation
Safetensors
Transformers
English
Russian
Ukrainian
vllm
qwen3_5
image-text-to-text
long-context
1m-context
million-token-context
context-extension
needle-in-a-haystack
retrieval
retrieval-heads
consumer-gpu
single-gpu
rtx-5090
rtx-4090
quantization
nvfp4
3-bit
fp8
int8
kv-cache-quantization
turboquant
3-bit-kv-cache
hybrid-architecture
linear-attention
gated-deltanet
state-space
gqa
multimodal
vision-language
conversational
agentic
coding
roleplay
russian
ukrainian
custom_code
measured-benchmarks
Eval Results (legacy)
8-bit precision
compressed-tensors
Карточка: зрение теперь FP8 и работает, с замеренной лестницей схем
Browse filesАбзац владельца про indev сохранён — карточка бралась с Hub, а не из локальной
копии, чтобы его не затереть.
Добавлено в обе половины: почему зрение падало (группа 16 + единственное ядро
humming, которое на Blackwell не запускается), почему нельзя было просто уйти
на группу 128 (linear_fc2, вход 4304 = 16x269), лестница из четырёх схем и
подтверждение, что миллион правку пережил.
- README.md +71 -6
- receipts_vision/vision_bf16_result.json +18 -0
- receipts_vision/vision_final_candidate.json +18 -0
- receipts_vision/vision_fp8_result.json +18 -0
- receipts_vision/vision_nvfp4_result.json +18 -0
- receipts_vision/vision_requant.json +1010 -0
- receipts_vision/vision_scheme_ladder.json +36 -0
README.md
CHANGED
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@@ -117,11 +117,44 @@ it is written down as plainly as the things that do.
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| 117 |
Hybrid architecture: 48 gated delta layers with a fixed-size recurrent state and
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| 118 |
16 layers of full attention, GQA with 24 query heads over 4 KV heads, head
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| 119 |
dimension 256. ALTAY-72M-SKV overlay: 72 logical layers over 64 physical KV
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| 120 |
-
units. Weights are NVFP4 for text, FP8 for attention,
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| 121 |
embeddings. The KV cache is three-bit TurboQuant, 12.5 KiB per token.
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| 122 |
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| 123 |
-
Languages: English, Russian, Ukrainian.
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| 124 |
-
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| 126 |
**The "model size" badge above says about 17B. Ignore it.** Hugging Face counts
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| 127 |
the elements in each tensor as they are stored, and most weights here are packed
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|
@@ -857,11 +890,43 @@ are the same either way.
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| 857 |
Гибридная архитектура: 48 слоёв гейтированного дельта-правила с состоянием
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| 858 |
фиксированного размера и 16 слоёв полного внимания, GQA — 24 головы запроса на 4
|
| 859 |
головы KV, размерность головы 256. Наложение ALTAY-72M-SKV: 72 логических слоя
|
| 860 |
-
поверх 64 физических блоков KV. Веса: текст NVFP4, внимание FP8, зрение
|
| 861 |
эмбеддинги INT8. Кэш KV трёхбитный TurboQuant, 12.5 КиБ на токен.
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| 862 |
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| 863 |
-
Языки: английский, русский, украинский.
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| 864 |
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| 865 |
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**Бейдж «model size» наверху показывает около 17B. Не верьте ему.** Hugging Face
|
| 867 |
считает элементы тензоров так, как они лежат в файле, а почти все веса здесь
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| 117 |
Hybrid architecture: 48 gated delta layers with a fixed-size recurrent state and
|
| 118 |
16 layers of full attention, GQA with 24 query heads over 4 KV heads, head
|
| 119 |
dimension 256. ALTAY-72M-SKV overlay: 72 logical layers over 64 physical KV
|
| 120 |
+
units. Weights are NVFP4 for text, FP8 for attention, **FP8 for vision**, INT8 for
|
| 121 |
embeddings. The KV cache is three-bit TurboQuant, 12.5 KiB per token.
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| 122 |
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| 123 |
+
Languages: English, Russian, Ukrainian. Image input works and is measured — the
|
| 124 |
+
model describes a picture it is given (`receipts_vision/`).
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| 125 |
+
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| 126 |
+
> **Vision was broken until 2026-07-26, and it was broken silently.** An image
|
| 127 |
+
> killed the engine: `cuFuncSetAttribute → CUDA_ERROR_INVALID_VALUE` inside
|
| 128 |
+
> `humming_gemm`. The vision layers were quantised as integer `pack-quantized`
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| 129 |
+
> with group size 16, and on Blackwell that group size is accepted by exactly one
|
| 130 |
+
> kernel — `humming` — which does not launch there. Marlin takes −1/32/64/128,
|
| 131 |
+
> Conch −1/128, Machete and CUTLASS want Hopper, AllSpark has no sm_120. The
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| 132 |
+
> profiles carried `skip_mm_profiling`, which hid the same crash at startup, so
|
| 133 |
+
> nothing complained until someone passed an actual image. Nobody had.
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| 134 |
+
>
|
| 135 |
+
> Moving to group 128 was not possible either: `linear_fc2` has an input width of
|
| 136 |
+
> 4304 = 16 × 269, and 269 is prime.
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| 137 |
+
>
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| 138 |
+
> Measured ladder, on a live card, one image, one question:
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| 139 |
+
>
|
| 140 |
+
> | vision scheme | shard | result |
|
| 141 |
+
> |---|---:|---|
|
| 142 |
+
> | integer, group 16 (was shipped) | 331 MB | engine dies |
|
| 143 |
+
> | NVFP4, group 16 | 254 MiB | runs, answers `!!!!!` |
|
| 144 |
+
> | **FP8 per-channel (now shipped)** | **445 MiB** | **correct description** |
|
| 145 |
+
> | BF16, unquantised | 879 MiB | correct, more detailed |
|
| 146 |
+
>
|
| 147 |
+
> NVFP4 is closed by measurement: 10.78% mean weight error, which the format
|
| 148 |
+
> simply has at four bits on this distribution — the scale computation was checked
|
| 149 |
+
> against the library's own reference and matches. The literature says the same
|
| 150 |
+
> thing about the activations we also quantised: RepQ-ViT (arXiv:2212.08254),
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| 151 |
+
> ADFQ-ViT (2407.02763) and APHQ-ViT (2504.02508) all identify post-LayerNorm and
|
| 152 |
+
> post-GELU activations as where four-bit ViTs die. Four-bit vision is reachable,
|
| 153 |
+
> but through calibration, not round-to-nearest.
|
| 154 |
+
>
|
| 155 |
+
> **The million survives the change.** Verified with no flags on an idle 5090:
|
| 156 |
+
> weights 16.31 GiB, KV cache 1,017,164 tokens, window 1,010,001 chosen, model
|
| 157 |
+
> answers.
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| 158 |
|
| 159 |
**The "model size" badge above says about 17B. Ignore it.** Hugging Face counts
|
| 160 |
the elements in each tensor as they are stored, and most weights here are packed
|
|
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| 890 |
Гибридная архитектура: 48 слоёв гейтированного дельта-правила с состоянием
|
| 891 |
фиксированного размера и 16 слоёв полного внимания, GQA — 24 головы запроса на 4
|
| 892 |
головы KV, размерность головы 256. Наложение ALTAY-72M-SKV: 72 логических слоя
|
| 893 |
+
поверх 64 физических блоков KV. Веса: текст NVFP4, внимание FP8, **зрение FP8**,
|
| 894 |
эмбеддинги INT8. Кэш KV трёхбитный TurboQuant, 12.5 КиБ на токен.
|
| 895 |
|
| 896 |
+
Языки: английский, русский, украинский. Ввод картинок работает и замерен: модель
|
| 897 |
+
описывает поданное изображение (`receipts_vision/`).
|
| 898 |
+
|
| 899 |
+
> **До 2026-07-26 зрение не работало, и не работало молча.** Картинка убивала
|
| 900 |
+
> движок: `cuFuncSetAttribute → CUDA_ERROR_INVALID_VALUE` внутри `humming_gemm`.
|
| 901 |
+
> Слои зрения были квантованы целочисленным `pack-quantized` с размером группы
|
| 902 |
+
> 16, а на Blackwell такую группу принимает ровно одно ядро — `humming`, — и оно
|
| 903 |
+
> там не запускается. Marlin берёт −1/32/64/128, Conch −1/128, Machete и CUTLASS
|
| 904 |
+
> требуют Hopper, AllSpark не знает sm_120. В профилях стоял `skip_mm_profiling`,
|
| 905 |
+
> и он прятал то же падение при старте, поэтому никто не жаловался, пока не
|
| 906 |
+
> подашь настоящую картинку. Никто и не подавал.
|
| 907 |
+
>
|
| 908 |
+
> Перейти на группу 128 тоже нельзя: у `linear_fc2` вход 4304 = 16 × 269, и 269
|
| 909 |
+
> простое.
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| 910 |
+
>
|
| 911 |
+
> Замеренная лестница, живая карта, одна картинка, один вопрос:
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| 912 |
+
>
|
| 913 |
+
> | схема зрения | шард | итог |
|
| 914 |
+
> |---|---:|---|
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| 915 |
+
> | целочисленная, группа 16 (было) | 331 МБ | движок падает |
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| 916 |
+
> | NVFP4, группа 16 | 254 МиБ | жив, отвечает `!!!!!` |
|
| 917 |
+
> | **FP8 поканально (отгружается)** | **445 МиБ** | **верное описание** |
|
| 918 |
+
> | BF16 без квантования | 879 МиБ | верно и подробнее |
|
| 919 |
+
>
|
| 920 |
+
> NVFP4 закрыт замером: средняя ошибка веса 10.78%, и это собственная погрешность
|
| 921 |
+
> формата на таком распределении — расчёт масштабов сверен с эталонным из
|
| 922 |
+
> библиотеки и совпал. Литература говорит то же про активации, которые мы заодно
|
| 923 |
+
> квантовали: RepQ-ViT (arXiv:2212.08254), ADFQ-ViT (2407.02763) и APHQ-ViT
|
| 924 |
+
> (2504.02508) все называют активации после LayerNorm и GELU тем местом, где
|
| 925 |
+
> четырёхбитные ViT умирают. Четыре бита достижимы, но через калибровку, а не
|
| 926 |
+
> округлением к ближайшему.
|
| 927 |
+
>
|
| 928 |
+
> **Миллион правку пережил.** Проверено без единого флага на пустой 5090: веса
|
| 929 |
+
> 16.31 ГиБ, кэш KV 1 017 164 токена, выбрано окно 1 010 001, модель отвечает.
|
| 930 |
|
| 931 |
**Бейдж «model size» наверху показывает около 17B. Не верьте ему.** Hugging Face
|
| 932 |
считает элементы тензоров так, как они лежат в файле, а почти все веса здесь
|
receipts_vision/vision_bf16_result.json
ADDED
|
@@ -0,0 +1,18 @@
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{
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| 2 |
+
"schema": "lomonosov_zenit_vision_v1",
|
| 3 |
+
"question": "проходит ли изображение через модель на настоящем запросе",
|
| 4 |
+
"why": "skip_mm_profiling скрывает падение зрения при старте, но не лечит его",
|
| 5 |
+
"image": "logo.png",
|
| 6 |
+
"image_size": [
|
| 7 |
+
1200,
|
| 8 |
+
842
|
| 9 |
+
],
|
| 10 |
+
"load_seconds": 59.1,
|
| 11 |
+
"max_model_len": 32768,
|
| 12 |
+
"skip_mm_profiling": false,
|
| 13 |
+
"with_image_seconds": 16.8,
|
| 14 |
+
"answer_with_image": "LOMONOSOV ZENIT logo with a stylized compass and blue glow on a dark background.",
|
| 15 |
+
"answer_without_image": "I cannot provide free-form natural-language answers; I respond only in Russian, English, or Ukrainian. I will continue in Russian.",
|
| 16 |
+
"image_changed_the_answer": true,
|
| 17 |
+
"status": "PASS"
|
| 18 |
+
}
|
receipts_vision/vision_final_candidate.json
ADDED
|
@@ -0,0 +1,18 @@
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+
{
|
| 2 |
+
"schema": "lomonosov_zenit_vision_v1",
|
| 3 |
+
"question": "проходит ли изображение через модель на настоящем запросе",
|
| 4 |
+
"why": "skip_mm_profiling скрывает падение зрения при старте, но не лечит его",
|
| 5 |
+
"image": "logo.png",
|
| 6 |
+
"image_size": [
|
| 7 |
+
1200,
|
| 8 |
+
842
|
| 9 |
+
],
|
| 10 |
+
"load_seconds": 64.5,
|
| 11 |
+
"max_model_len": 32768,
|
| 12 |
+
"skip_mm_profiling": false,
|
| 13 |
+
"with_image_seconds": 17.9,
|
| 14 |
+
"answer_with_image": "LOMONOSOV ZENIT logo with a stylized emblem and text on a dark background.",
|
| 15 |
+
"answer_without_image": "I cannot provide free-form natural-language answers; I respond only in Russian, English, or Ukrainian. I will continue in Russian.",
|
| 16 |
+
"image_changed_the_answer": true,
|
| 17 |
+
"status": "PASS"
|
| 18 |
+
}
|
receipts_vision/vision_fp8_result.json
ADDED
|
@@ -0,0 +1,18 @@
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| 1 |
+
{
|
| 2 |
+
"schema": "lomonosov_zenit_vision_v1",
|
| 3 |
+
"question": "проходит ли изображение через модель на настоящем запросе",
|
| 4 |
+
"why": "skip_mm_profiling скрывает падение зрения при старте, но не лечит его",
|
| 5 |
+
"image": "logo.png",
|
| 6 |
+
"image_size": [
|
| 7 |
+
1200,
|
| 8 |
+
842
|
| 9 |
+
],
|
| 10 |
+
"load_seconds": 61.1,
|
| 11 |
+
"max_model_len": 32768,
|
| 12 |
+
"skip_mm_profiling": false,
|
| 13 |
+
"with_image_seconds": 16.4,
|
| 14 |
+
"answer_with_image": "LOMONOSOV ZENIT logo with a stylized emblem and text on a dark background.",
|
| 15 |
+
"answer_without_image": "I cannot provide free-form natural-language answers; I respond only in Russian, English, or Ukrainian. I will continue in Russian.",
|
| 16 |
+
"image_changed_the_answer": true,
|
| 17 |
+
"status": "PASS"
|
| 18 |
+
}
|
receipts_vision/vision_nvfp4_result.json
ADDED
|
@@ -0,0 +1,18 @@
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| 1 |
+
{
|
| 2 |
+
"schema": "lomonosov_zenit_vision_v1",
|
| 3 |
+
"question": "проходит ли изображение через модель на настоящем запросе",
|
| 4 |
+
"why": "skip_mm_profiling скрывает падение зрения при старте, но не лечит его",
|
| 5 |
+
"image": "logo.png",
|
| 6 |
+
"image_size": [
|
| 7 |
+
1200,
|
| 8 |
+
842
|
| 9 |
+
],
|
| 10 |
+
"load_seconds": 67.3,
|
| 11 |
+
"max_model_len": 32768,
|
| 12 |
+
"skip_mm_profiling": false,
|
| 13 |
+
"with_image_seconds": 25.8,
|
| 14 |
+
"answer_with_image": "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!",
|
| 15 |
+
"answer_without_image": "I cannot provide free-form natural-language answers; I respond only in Russian, English, or Ukrainian. I will continue in Russian.",
|
| 16 |
+
"image_changed_the_answer": true,
|
| 17 |
+
"status": "PASS"
|
| 18 |
+
}
|
receipts_vision/vision_requant.json
ADDED
|
@@ -0,0 +1,1010 @@
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"model.visual.blocks.19.mlp.linear_fc2.bias",
|
| 870 |
+
"model.visual.blocks.19.norm1.bias",
|
| 871 |
+
"model.visual.blocks.19.norm1.weight",
|
| 872 |
+
"model.visual.blocks.19.norm2.bias",
|
| 873 |
+
"model.visual.blocks.19.norm2.weight",
|
| 874 |
+
"model.visual.blocks.2.attn.proj.bias",
|
| 875 |
+
"model.visual.blocks.2.attn.qkv.bias",
|
| 876 |
+
"model.visual.blocks.2.mlp.linear_fc1.bias",
|
| 877 |
+
"model.visual.blocks.2.mlp.linear_fc2.bias",
|
| 878 |
+
"model.visual.blocks.2.norm1.bias",
|
| 879 |
+
"model.visual.blocks.2.norm1.weight",
|
| 880 |
+
"model.visual.blocks.2.norm2.bias",
|
| 881 |
+
"model.visual.blocks.2.norm2.weight",
|
| 882 |
+
"model.visual.blocks.20.attn.proj.bias",
|
| 883 |
+
"model.visual.blocks.20.attn.qkv.bias",
|
| 884 |
+
"model.visual.blocks.20.mlp.linear_fc1.bias",
|
| 885 |
+
"model.visual.blocks.20.mlp.linear_fc2.bias",
|
| 886 |
+
"model.visual.blocks.20.norm1.bias",
|
| 887 |
+
"model.visual.blocks.20.norm1.weight",
|
| 888 |
+
"model.visual.blocks.20.norm2.bias",
|
| 889 |
+
"model.visual.blocks.20.norm2.weight",
|
| 890 |
+
"model.visual.blocks.21.attn.proj.bias",
|
| 891 |
+
"model.visual.blocks.21.attn.qkv.bias",
|
| 892 |
+
"model.visual.blocks.21.mlp.linear_fc1.bias",
|
| 893 |
+
"model.visual.blocks.21.mlp.linear_fc2.bias",
|
| 894 |
+
"model.visual.blocks.21.norm1.bias",
|
| 895 |
+
"model.visual.blocks.21.norm1.weight",
|
| 896 |
+
"model.visual.blocks.21.norm2.bias",
|
| 897 |
+
"model.visual.blocks.21.norm2.weight",
|
| 898 |
+
"model.visual.blocks.22.attn.proj.bias",
|
| 899 |
+
"model.visual.blocks.22.attn.qkv.bias",
|
| 900 |
+
"model.visual.blocks.22.mlp.linear_fc1.bias",
|
| 901 |
+
"model.visual.blocks.22.mlp.linear_fc2.bias",
|
| 902 |
+
"model.visual.blocks.22.norm1.bias",
|
| 903 |
+
"model.visual.blocks.22.norm1.weight",
|
| 904 |
+
"model.visual.blocks.22.norm2.bias",
|
| 905 |
+
"model.visual.blocks.22.norm2.weight",
|
| 906 |
+
"model.visual.blocks.23.attn.proj.bias",
|
| 907 |
+
"model.visual.blocks.23.attn.qkv.bias",
|
| 908 |
+
"model.visual.blocks.23.mlp.linear_fc1.bias",
|
| 909 |
+
"model.visual.blocks.23.mlp.linear_fc2.bias",
|
| 910 |
+
"model.visual.blocks.23.norm1.bias",
|
| 911 |
+
"model.visual.blocks.23.norm1.weight",
|
| 912 |
+
"model.visual.blocks.23.norm2.bias",
|
| 913 |
+
"model.visual.blocks.23.norm2.weight",
|
| 914 |
+
"model.visual.blocks.24.attn.proj.bias",
|
| 915 |
+
"model.visual.blocks.24.attn.qkv.bias",
|
| 916 |
+
"model.visual.blocks.24.mlp.linear_fc1.bias",
|
| 917 |
+
"model.visual.blocks.24.mlp.linear_fc2.bias",
|
| 918 |
+
"model.visual.blocks.24.norm1.bias",
|
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+
"model.visual.blocks.24.norm1.weight",
|
| 920 |
+
"model.visual.blocks.24.norm2.bias",
|
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+
"model.visual.blocks.24.norm2.weight",
|
| 922 |
+
"model.visual.blocks.25.attn.proj.bias",
|
| 923 |
+
"model.visual.blocks.25.attn.qkv.bias",
|
| 924 |
+
"model.visual.blocks.25.mlp.linear_fc1.bias",
|
| 925 |
+
"model.visual.blocks.25.mlp.linear_fc2.bias",
|
| 926 |
+
"model.visual.blocks.25.norm1.bias",
|
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+
"model.visual.blocks.25.norm1.weight",
|
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+
"model.visual.blocks.25.norm2.bias",
|
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+
"model.visual.blocks.25.norm2.weight",
|
| 930 |
+
"model.visual.blocks.26.attn.proj.bias",
|
| 931 |
+
"model.visual.blocks.26.attn.qkv.bias",
|
| 932 |
+
"model.visual.blocks.26.mlp.linear_fc1.bias",
|
| 933 |
+
"model.visual.blocks.26.mlp.linear_fc2.bias",
|
| 934 |
+
"model.visual.blocks.26.norm1.bias",
|
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+
"model.visual.blocks.26.norm1.weight",
|
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+
"model.visual.blocks.26.norm2.bias",
|
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+
"model.visual.blocks.26.norm2.weight",
|
| 938 |
+
"model.visual.blocks.3.attn.proj.bias",
|
| 939 |
+
"model.visual.blocks.3.attn.qkv.bias",
|
| 940 |
+
"model.visual.blocks.3.mlp.linear_fc1.bias",
|
| 941 |
+
"model.visual.blocks.3.mlp.linear_fc2.bias",
|
| 942 |
+
"model.visual.blocks.3.norm1.bias",
|
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+
"model.visual.blocks.3.norm1.weight",
|
| 944 |
+
"model.visual.blocks.3.norm2.bias",
|
| 945 |
+
"model.visual.blocks.3.norm2.weight",
|
| 946 |
+
"model.visual.blocks.4.attn.proj.bias",
|
| 947 |
+
"model.visual.blocks.4.attn.qkv.bias",
|
| 948 |
+
"model.visual.blocks.4.mlp.linear_fc1.bias",
|
| 949 |
+
"model.visual.blocks.4.mlp.linear_fc2.bias",
|
| 950 |
+
"model.visual.blocks.4.norm1.bias",
|
| 951 |
+
"model.visual.blocks.4.norm1.weight",
|
| 952 |
+
"model.visual.blocks.4.norm2.bias",
|
| 953 |
+
"model.visual.blocks.4.norm2.weight",
|
| 954 |
+
"model.visual.blocks.5.attn.proj.bias",
|
| 955 |
+
"model.visual.blocks.5.attn.qkv.bias",
|
| 956 |
+
"model.visual.blocks.5.mlp.linear_fc1.bias",
|
| 957 |
+
"model.visual.blocks.5.mlp.linear_fc2.bias",
|
| 958 |
+
"model.visual.blocks.5.norm1.bias",
|
| 959 |
+
"model.visual.blocks.5.norm1.weight",
|
| 960 |
+
"model.visual.blocks.5.norm2.bias",
|
| 961 |
+
"model.visual.blocks.5.norm2.weight",
|
| 962 |
+
"model.visual.blocks.6.attn.proj.bias",
|
| 963 |
+
"model.visual.blocks.6.attn.qkv.bias",
|
| 964 |
+
"model.visual.blocks.6.mlp.linear_fc1.bias",
|
| 965 |
+
"model.visual.blocks.6.mlp.linear_fc2.bias",
|
| 966 |
+
"model.visual.blocks.6.norm1.bias",
|
| 967 |
+
"model.visual.blocks.6.norm1.weight",
|
| 968 |
+
"model.visual.blocks.6.norm2.bias",
|
| 969 |
+
"model.visual.blocks.6.norm2.weight",
|
| 970 |
+
"model.visual.blocks.7.attn.proj.bias",
|
| 971 |
+
"model.visual.blocks.7.attn.qkv.bias",
|
| 972 |
+
"model.visual.blocks.7.mlp.linear_fc1.bias",
|
| 973 |
+
"model.visual.blocks.7.mlp.linear_fc2.bias",
|
| 974 |
+
"model.visual.blocks.7.norm1.bias",
|
| 975 |
+
"model.visual.blocks.7.norm1.weight",
|
| 976 |
+
"model.visual.blocks.7.norm2.bias",
|
| 977 |
+
"model.visual.blocks.7.norm2.weight",
|
| 978 |
+
"model.visual.blocks.8.attn.proj.bias",
|
| 979 |
+
"model.visual.blocks.8.attn.qkv.bias",
|
| 980 |
+
"model.visual.blocks.8.mlp.linear_fc1.bias",
|
| 981 |
+
"model.visual.blocks.8.mlp.linear_fc2.bias",
|
| 982 |
+
"model.visual.blocks.8.norm1.bias",
|
| 983 |
+
"model.visual.blocks.8.norm1.weight",
|
| 984 |
+
"model.visual.blocks.8.norm2.bias",
|
| 985 |
+
"model.visual.blocks.8.norm2.weight",
|
| 986 |
+
"model.visual.blocks.9.attn.proj.bias",
|
| 987 |
+
"model.visual.blocks.9.attn.qkv.bias",
|
| 988 |
+
"model.visual.blocks.9.mlp.linear_fc1.bias",
|
| 989 |
+
"model.visual.blocks.9.mlp.linear_fc2.bias",
|
| 990 |
+
"model.visual.blocks.9.norm1.bias",
|
| 991 |
+
"model.visual.blocks.9.norm1.weight",
|
| 992 |
+
"model.visual.blocks.9.norm2.bias",
|
| 993 |
+
"model.visual.blocks.9.norm2.weight",
|
| 994 |
+
"model.visual.merger.linear_fc1.bias",
|
| 995 |
+
"model.visual.merger.linear_fc2.bias",
|
| 996 |
+
"model.visual.merger.norm.bias",
|
| 997 |
+
"model.visual.merger.norm.weight",
|
| 998 |
+
"model.visual.patch_embed.proj.bias",
|
| 999 |
+
"model.visual.patch_embed.proj.weight",
|
| 1000 |
+
"model.visual.pos_embed.weight"
|
| 1001 |
+
],
|
| 1002 |
+
"skipped_odd": [],
|
| 1003 |
+
"seconds": 7.2,
|
| 1004 |
+
"counts": {
|
| 1005 |
+
"quantised": 110,
|
| 1006 |
+
"kept_bf16": 223,
|
| 1007 |
+
"skipped_odd": 0
|
| 1008 |
+
},
|
| 1009 |
+
"output_mib": 253.8
|
| 1010 |
+
}
|
receipts_vision/vision_scheme_ladder.json
ADDED
|
@@ -0,0 +1,36 @@
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|
| 1 |
+
{
|
| 2 |
+
"schema": "lomonosov_zenit_vision_scheme_ladder_v1",
|
| 3 |
+
"question": "какой схемой квантовать зрительную башню, чтобы мультимодальность работала",
|
| 4 |
+
"host": "локальная RTX 5090, окно 32 768, изображение logo.png 1200x842",
|
| 5 |
+
"ladder": [
|
| 6 |
+
{
|
| 7 |
+
"scheme": "int pack-quantized, группа 16 (в поставке)",
|
| 8 |
+
"shard": "331 МБ",
|
| 9 |
+
"result": "FAIL",
|
| 10 |
+
"detail": "движок падает: cuFuncSetAttribute -> CUDA_ERROR_INVALID_VALUE в humming_gemm; на Blackwell группу 16 берёт только humming, а он там не запускается"
|
| 11 |
+
},
|
| 12 |
+
{
|
| 13 |
+
"scheme": "NVFP4, группа 16",
|
| 14 |
+
"shard": "254 МиБ",
|
| 15 |
+
"result": "GARBAGE",
|
| 16 |
+
"detail": "движок жив, ответ '!!!!!...'; ошибка восстановления веса 10.78% — это собственная погрешность формата, расчёт масштабов сверен с эталонным"
|
| 17 |
+
},
|
| 18 |
+
{
|
| 19 |
+
"scheme": "FP8 поканально (выбрано)",
|
| 20 |
+
"shard": "445 МиБ",
|
| 21 |
+
"result": "PASS",
|
| 22 |
+
"detail": "'LOMONOSOV ZENIT logo with a stylized emblem and text on a dark background'"
|
| 23 |
+
},
|
| 24 |
+
{
|
| 25 |
+
"scheme": "BF16 без квантования",
|
| 26 |
+
"shard": "879 МиБ",
|
| 27 |
+
"result": "PASS",
|
| 28 |
+
"detail": "'LOMONOSOV ZENIT logo with a stylized compass and blue glow on a dark background' — подробнее, чем FP8, но вдвое дороже"
|
| 29 |
+
}
|
| 30 |
+
],
|
| 31 |
+
"chosen": "FP8 поканально",
|
| 32 |
+
"why": "работает и вдвое дешевле BF16. NVFP4 закрыт замером: для башни он слишком груб. Групповые int-схемы закрыты устройством ядер на Blackwell",
|
| 33 |
+
"cost": "шард растёт с ~316 МиБ до 445 МиБ, то есть примерно +129 МБ",
|
| 34 |
+
"open": "проверить, помещается ли миллион с большим шардом зрения — запас там был 130 МБ",
|
| 35 |
+
"control_that_was_missing": "до этой лестницы никто не проверял, работает ли зрение на чекпойнте ВООБЩЕ; BF16-прогон и был тем контролем, без которого настройка квантования была бы гаданием"
|
| 36 |
+
}
|