Instructions to use Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV", trust_remote_code=True) model = AutoModelForImageTextToText.from_pretrained("Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV", trust_remote_code=True, device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV
- SGLang
How to use Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV with Docker Model Runner:
docker model run hf.co/Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV
Fix profile selection on 24 GB cards: measured thresholds, verified end to end
Browse filesThe no-flags launch did not work on a 24 GB card. Three separate causes, now fixed and measured.
1. requires_free_bytes on long_393k and long_262k were estimates, and both were low by about 1.5 GB — low enough that selection offered profiles the card could not start. A ballast ladder found the real thresholds: long_393k needs 25,572,315,586 (measured to 199 MB), long_262k needs 23,972,315,586 (measured to 510 MB). The headroom above weights plus arena came out identical for both profiles to within 127 KB, so the figures are a measurement rather than a guess.
2. long_262k declared no gpu_memory_utilization at all, so the clamp had nothing to lower and vLLM's 0.92 default asked for more than a short card has. It now declares 0.95 and gets clamped like the others. Both fixes are required together; each alone was measured and does not work.
3. raw_1010k is unchanged. By the measured constant it would require more than a rented 5090 has free, which would reject the flagship path that demonstrably works. Its threshold is being measured separately.
Verified end to end at four memory levels with the exact release config and no profile environment variables: 26.57 GB free selects 393,216 and runs; 25.67 and 24.47 select 262,144 and run; 23.17 fits no profile and the log says so by name and number. Receipts in receipts/profiles/.
- README.md +47 -27
- config.json +4 -3
- receipts/profiles/fix_full.json +19 -0
- receipts/profiles/rel_23700.json +11 -0
- receipts/profiles/rel_25000.json +19 -0
- receipts/profiles/rel_26200.json +19 -0
- receipts/profiles/rel_27100.json +19 -0
- receipts/profiles/thr262_23000.json +11 -0
- receipts/profiles/thr262_24500.json +19 -0
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Anything you pass on the command line always beats the profile, and every
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substitution is logged.
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| profile | window | memory
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| `raw_1010k` | 1,010,001 |
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| `long_393k` | 393,216 |
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| `long_262k` | 262,144 |
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A profile wider than the trained 262,144 carries a **position plan**: exact
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positions below 262,144, integer division by four above it. Inside the trained
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Любое значение, заданное вами в командной строке, всегда сильнее профиля, и
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каждая подстановка пишется в лог.
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| `raw_1010k` | 1 010 001 |
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| `long_393k` | 393 216 |
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| `long_262k` | 262 144 |
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**Эти
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Профиль шире обученных 262 144 везёт с собой **план позиций**: до 262 144 позиции
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точные, за этой границей — целочисленное деление на четыре. Внутри обученного
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Anything you pass on the command line always beats the profile, and every
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substitution is logged.
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| profile | window | free memory required | measured |
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| `raw_1010k` | 1,010,001 | 32.18 GB | works headless on a 5090 |
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| `long_393k` | 393,216 | **25.57 GB** | threshold measured to 199 MB |
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| `long_262k` | 262,144 | **23.97 GB** | threshold measured to 510 MB |
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**These two requirements are measured, not estimated, and that is a change.** Until
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2026-07-26 they were estimates, and both were low by about 1.5 GB — low enough that
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selection offered profiles the card could not start. A ballast ladder on a rented
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5090 found the real thresholds by holding memory at successive levels and giving the
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bare command each time.
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The whole selection is now verified end to end at four memory levels: 26.57 GB free
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selects the 393,216 window and runs; 25.67 and 24.47 select 262,144 and run; 23.17
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fits no profile at all, and the log says so by name and number before it stops. That
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last case is correct rather than broken — the weights alone are 16.44 GiB, and a
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card with 21 GiB free cannot run this model at any window.
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`long_262k` also carries an explicit `gpu_memory_utilization` now. Without it the
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clamp had nothing to lower and vLLM's 0.92 default asked for more than a short card
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has. Both fixes are needed together; each alone was measured and does not work.
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`raw_1010k`'s requirement is being measured as this card is written. It is the one
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number here still inherited from the estimate.
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A profile wider than the trained 262,144 carries a **position plan**: exact
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positions below 262,144, integer division by four above it. Inside the trained
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Любое значение, заданное вами в командной строке, всегда сильнее профиля, и
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каждая подстановка пишется в лог.
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| профиль | окно | требуется свободной памяти | замер |
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| `raw_1010k` | 1 010 001 | 32.18 ГБ | работает на безголовой 5090 |
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| `long_393k` | 393 216 | **25.57 ГБ** | порог замерен с точностью 199 МБ |
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| `long_262k` | 262 144 | **23.97 ГБ** | порог замерен с точностью 510 МБ |
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**Эти два требования замерены, а не оценены, и это ��зменение.** До 2026-07-26 они
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были оценками, и обе занижены примерно на 1.5 ГБ — настолько, что отбор предлагал
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профили, которые карта не поднимет. Балластная лестница на арендованной 5090 нашла
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настоящие пороги: память удерживалась на последовательных уровнях, и на каждом
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давалась голая команда.
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Весь отбор теперь проверен сквозным прогоном на четырёх уровнях памяти: при 26.57 ГБ
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свободных выбирается окно 393 216 и работает; при 25.67 и 24.47 выбирается 262 144 и
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работает; при 23.17 не подходит ни один профиль, и лог говорит об этом поимённо и с
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цифрами, прежде чем остановиться. Последний случай правильный, а не сломанный: одни
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веса занимают 16.44 ГиБ, и карта с 21 ГиБ свободных не поднимет эту модель ни при
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каком окне.
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У `long_262k` теперь есть и явная `gpu_memory_utilization`. Без неё прижимать было
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нечего, и умолчание vLLM 0.92 просило больше, чем есть на тесной карте. Обе правки
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нужны вместе — каждая по отдельности замерена и не работает.
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Требование `raw_1010k` меряется прямо сейчас. Это единственное число здесь, всё ещё
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доставшееся от оценки.
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Профиль шире обученных 262 144 везёт с собой **план позиций**: до 262 144 позиции
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точные, за этой границей — целочисленное деление на четыре. Внутри обученного
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"max_num_seqs": 1,
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"noosphere_mode": "raw",
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"provenance": "prefill measured on RTX 5090: 147.9 s for 262,144 tokens (1772 tok/s) with a pinned arena, GPU at 99%. async_scheduling deliberately left unset: measured 168.9 s off against 172.1 s on at this length, a difference inside run-to-run noise. Арена задана явно: 3,600,000,000 байт, то есть 281,579 токенов ёмкости при окне 262,144 - запас есть, как и у миллиона. Это не только про запас. Без явной арены vLLM гоняет свой профилирующий проход, и на нём запуск падал в humming_gemm с CUDA_ERROR_INVALID_VALUE: ядро просило больше разделяемой памяти, чем разрешает карта. Все наши замеры этого не видели ровно потому, что всегда задавали арену и профилирование пропускалось.",
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"long_393k": {
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"description": "Window for 24 GiB cards. Measured on an RTX 4090: with the engine at 0.95 the KV arena holds 452,522 tokens, so this window fits with room to spare.",
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"max_num_seqs": 1,
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"noosphere_mode": "raw",
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"provenance": "sized from the RTX 4090 run, where the engine reported a KV arena of 452,522 tokens at gpu_memory_utilization 0.95 with 480 MB still free. The window is set below that capacity on purpose, and the stated requirement uses the more conservative of two overhead estimates (2.04 GiB against the 1.72 GiB the same run implies). Арена задана явно: 5,200,000,000 байт, то есть 406,726 токенов ёмкости при окне 393,216 - запас есть, как и у миллиона. Это не только про запас. Без явной арены vLLM гоняет свой профилирующий проход, и на нём запуск падал в humming_gemm с CUDA_ERROR_INVALID_VALUE: ядро просило больше разделяемой памяти, чем разрешает карта. Все наши замеры этого не видели ровно потому, что всегда задавали арену и профилирование пропускалось.",
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"requires_free_bytes":
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"skip_mm_profiling": true
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"raw_1010k": {
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"max_num_seqs": 1,
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"noosphere_mode": "raw",
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"provenance": "prefill measured on RTX 5090: 147.9 s for 262,144 tokens (1772 tok/s) with a pinned arena, GPU at 99%. async_scheduling deliberately left unset: measured 168.9 s off against 172.1 s on at this length, a difference inside run-to-run noise. Арена задана явно: 3,600,000,000 байт, то есть 281,579 токенов ёмкости при окне 262,144 - запас есть, как и у миллиона. Это не только про запас. Без явной арены vLLM гоняет свой профилирующий проход, и на нём запуск падал в humming_gemm с CUDA_ERROR_INVALID_VALUE: ядро просило больше разделяемой памяти, чем разрешает карта. Все наши замеры этого не видели ровно потому, что всегда задавали арену и профилирование пропускалось.",
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"requires_free_bytes": 23972315586,
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"skip_mm_profiling": true,
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"gpu_memory_utilization": 0.95
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},
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"long_393k": {
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"description": "Window for 24 GiB cards. Measured on an RTX 4090: with the engine at 0.95 the KV arena holds 452,522 tokens, so this window fits with room to spare.",
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"max_num_seqs": 1,
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"noosphere_mode": "raw",
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"provenance": "sized from the RTX 4090 run, where the engine reported a KV arena of 452,522 tokens at gpu_memory_utilization 0.95 with 480 MB still free. The window is set below that capacity on purpose, and the stated requirement uses the more conservative of two overhead estimates (2.04 GiB against the 1.72 GiB the same run implies). Арена задана явно: 5,200,000,000 байт, то есть 406,726 токенов ёмкости при окне 393,216 - запас есть, как и у миллиона. Это не только про запас. Без явной арены vLLM гоняет свой профилирующий проход, и на нём запуск падал в humming_gemm с CUDA_ERROR_INVALID_VALUE: ядро просило больше разделяемой памяти, чем разрешает карта. Все наши замеры этого не видели ровно потому, что всегда задавали арену и профилирование пропускалось.",
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"requires_free_bytes": 25572315586,
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"skip_mm_profiling": true
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},
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"raw_1010k": {
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{
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"schema": "lomonosov_zenit_flagless_ballast_v1",
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"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
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"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
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"gpu_total_bytes": 33668726784,
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"ballast_bytes": 7379615744,
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"free_bytes_seen_by_engine": 25229197312,
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"ada_total_bytes_for_reference": 25757220864,
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"load_seconds": 73.7,
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"chosen_max_model_len": 262144,
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"chosen_kv_cache_dtype": "turboquant_3bit_nc",
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"chosen_chunk": 4096,
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"gpu_memory_utilization": 0.723,
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"position_plan_env": {
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"ZENIT_NOOSPHERE_MODE": "raw"
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},
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"generated": "<think>\n\n</think>\n\nДА",
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"status": "PASS"
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}
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{
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"schema": "lomonosov_zenit_flagless_ballast_v1",
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"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
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"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
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"gpu_total_bytes": 33668726784,
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"ballast_bytes": 9436836608,
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"free_bytes_seen_by_engine": 23171891200,
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"ada_total_bytes_for_reference": 25757220864,
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"status": "FAIL",
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"error": "RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}"
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}
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{
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"schema": "lomonosov_zenit_flagless_ballast_v1",
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"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
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+
"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
|
| 5 |
+
"gpu_total_bytes": 33668726784,
|
| 6 |
+
"ballast_bytes": 8136836608,
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| 7 |
+
"free_bytes_seen_by_engine": 24472125440,
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| 8 |
+
"ada_total_bytes_for_reference": 25757220864,
|
| 9 |
+
"load_seconds": 49.9,
|
| 10 |
+
"chosen_max_model_len": 262144,
|
| 11 |
+
"chosen_kv_cache_dtype": "turboquant_3bit_nc",
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| 12 |
+
"chosen_chunk": 4096,
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| 13 |
+
"gpu_memory_utilization": 0.701,
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| 14 |
+
"position_plan_env": {
|
| 15 |
+
"ZENIT_NOOSPHERE_MODE": "raw"
|
| 16 |
+
},
|
| 17 |
+
"generated": "<think>\n\n</think>\n\nДА",
|
| 18 |
+
"status": "PASS"
|
| 19 |
+
}
|
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@@ -0,0 +1,19 @@
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+
{
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| 2 |
+
"schema": "lomonosov_zenit_flagless_ballast_v1",
|
| 3 |
+
"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
|
| 4 |
+
"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
|
| 5 |
+
"gpu_total_bytes": 33668726784,
|
| 6 |
+
"ballast_bytes": 6936836608,
|
| 7 |
+
"free_bytes_seen_by_engine": 25671696384,
|
| 8 |
+
"ada_total_bytes_for_reference": 25757220864,
|
| 9 |
+
"load_seconds": 76.2,
|
| 10 |
+
"chosen_max_model_len": 262144,
|
| 11 |
+
"chosen_kv_cache_dtype": "turboquant_3bit_nc",
|
| 12 |
+
"chosen_chunk": 4096,
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| 13 |
+
"gpu_memory_utilization": 0.736,
|
| 14 |
+
"position_plan_env": {
|
| 15 |
+
"ZENIT_NOOSPHERE_MODE": "raw"
|
| 16 |
+
},
|
| 17 |
+
"generated": "<think>\n\n</think>\n\nДА",
|
| 18 |
+
"status": "PASS"
|
| 19 |
+
}
|
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@@ -0,0 +1,19 @@
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| 1 |
+
{
|
| 2 |
+
"schema": "lomonosov_zenit_flagless_ballast_v1",
|
| 3 |
+
"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
|
| 4 |
+
"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
|
| 5 |
+
"gpu_total_bytes": 33668726784,
|
| 6 |
+
"ballast_bytes": 6036836608,
|
| 7 |
+
"free_bytes_seen_by_engine": 26571374592,
|
| 8 |
+
"ada_total_bytes_for_reference": 25757220864,
|
| 9 |
+
"load_seconds": 72.3,
|
| 10 |
+
"chosen_max_model_len": 393216,
|
| 11 |
+
"chosen_kv_cache_dtype": "turboquant_3bit_nc",
|
| 12 |
+
"chosen_chunk": 4096,
|
| 13 |
+
"gpu_memory_utilization": 0.762,
|
| 14 |
+
"position_plan_env": {
|
| 15 |
+
"ZENIT_NOOSPHERE_MODE": "raw"
|
| 16 |
+
},
|
| 17 |
+
"generated": "<think>\n\n</think>\n\nДА",
|
| 18 |
+
"status": "PASS"
|
| 19 |
+
}
|
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@@ -0,0 +1,11 @@
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| 1 |
+
{
|
| 2 |
+
"schema": "lomonosov_zenit_flagless_ballast_v1",
|
| 3 |
+
"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
|
| 4 |
+
"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
|
| 5 |
+
"gpu_total_bytes": 33668726784,
|
| 6 |
+
"ballast_bytes": 10136836608,
|
| 7 |
+
"free_bytes_seen_by_engine": 22471442432,
|
| 8 |
+
"ada_total_bytes_for_reference": 25757220864,
|
| 9 |
+
"status": "FAIL",
|
| 10 |
+
"error": "RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}"
|
| 11 |
+
}
|
|
@@ -0,0 +1,19 @@
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| 1 |
+
{
|
| 2 |
+
"schema": "lomonosov_zenit_flagless_ballast_v1",
|
| 3 |
+
"what_this_shows": "выбор профиля и запуск без флагов при свободной памяти уровня 4090",
|
| 4 |
+
"what_this_does_not_show": "кремний Ada; он замерен отдельно в ADA_SM89_RECEIPT, но не этой же командой",
|
| 5 |
+
"gpu_total_bytes": 33668726784,
|
| 6 |
+
"ballast_bytes": 8636836608,
|
| 7 |
+
"free_bytes_seen_by_engine": 23970906112,
|
| 8 |
+
"ada_total_bytes_for_reference": 25757220864,
|
| 9 |
+
"load_seconds": 55.0,
|
| 10 |
+
"chosen_max_model_len": 262144,
|
| 11 |
+
"chosen_kv_cache_dtype": "turboquant_3bit_nc",
|
| 12 |
+
"chosen_chunk": 4096,
|
| 13 |
+
"gpu_memory_utilization": 0.687,
|
| 14 |
+
"position_plan_env": {
|
| 15 |
+
"ZENIT_NOOSPHERE_MODE": "raw"
|
| 16 |
+
},
|
| 17 |
+
"generated": "<think>\n\n</think>\n\nДА",
|
| 18 |
+
"status": "PASS"
|
| 19 |
+
}
|