Instructions to use Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

LOMONOSOV-ZENIT-27B-1M-INDEV / ZENIT_EMBED_INT8_LINEAGE.json

Ddavidich

Убраны абсолютные пути с машин сборки из родословных

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	{
	"created_at": "2026-07-24T18:05:21.914296+00:00",
	"output_embed_bytes": 1291264368,
	"output_embed_sha256": "ae5d141105e76ee6fc8004cd567f16a9b6e3819ca31fe1ebbe0998989895f77c",
	"output_model": "Project Sirius Experimental/ZVEZDA/runtime-models/staging/zenit-final-mm-altay72-noosphere-nvfp4-v12-embed-int8",
	"promotion": "forbidden_until_raw1010k_and_regression_gates",
	"quantization": {
	"group_size": 128,
	"hidden": 5120,
	"num_bits": 8,
	"relative_rmse": 0.006514949766178725,
	"strategy": "group",
	"symmetric": true,
	"vocab": 248320
	},
	"quantized_tensor": "model.language_model.embed_tokens.weight",
	"roundtrip_check": {
	"checked_rows": 512,
	"max_relative_error": 0.011059836484491825,
	"mean_cosine": 0.9999786615371704,
	"mean_relative_error": 0.006649822928011417,
	"min_cosine": 0.9999395608901978
	},
	"runtime_source": "Project Sirius Experimental/ZVEZDA/KIMI Stuff/GPT 5.6 Sol Artifacts/runtime/model_owned_noosphere_v2",
	"saved_bytes": 1251532432,
	"schema": "lomonosov_zenit_embed_int8_v12_v1",
	"source_embed_bytes": 2542796800,
	"source_model": "Project Sirius Experimental/ZVEZDA/runtime-models/staging/zenit-final-mm-altay72-noosphere-nvfp4-v11-vision-selective-w8-w4-a16",
	"status": "BUILT_UNPROMOTED_REQUIRES_GPU_LOAD_AND_PARITY"
	}