Instructions to use Dmitriy-Zemskov/ViorikaLM-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dmitriy-Zemskov/ViorikaLM-1.2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Dmitriy-Zemskov/ViorikaLM-1.2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dmitriy-Zemskov/ViorikaLM-1.2B")
model = AutoModelForCausalLM.from_pretrained("Dmitriy-Zemskov/ViorikaLM-1.2B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dmitriy-Zemskov/ViorikaLM-1.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dmitriy-Zemskov/ViorikaLM-1.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dmitriy-Zemskov/ViorikaLM-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dmitriy-Zemskov/ViorikaLM-1.2B

SGLang

How to use Dmitriy-Zemskov/ViorikaLM-1.2B 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 "Dmitriy-Zemskov/ViorikaLM-1.2B" \
    --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": "Dmitriy-Zemskov/ViorikaLM-1.2B",
		"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 "Dmitriy-Zemskov/ViorikaLM-1.2B" \
        --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": "Dmitriy-Zemskov/ViorikaLM-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dmitriy-Zemskov/ViorikaLM-1.2B with Docker Model Runner:
```
docker model run hf.co/Dmitriy-Zemskov/ViorikaLM-1.2B
```

🌌 ViorikaLM 1.2B (v1.0)

Это кастомная модель (LLM), обученная на моем железе. Совпадения с датасетами или чем то другим это случайность. АХТУНГ!!! МОДЕЛЬ ПРЕДОБУЧЕНА ДО СРЕДНЕГО УРОВНЯ!!! ДООБУЧЕНИЕ НА ВАШИХ ДАННЫХ ЖЕЛАТЕЛЬНО!!!

Подробнее:

⚙️ Детали модели

Архитектура: используется LLAMA, но модель своя.
Параметры: 1.2B
Изначально рандомные веса, были обучены.
Язык: Русский, только русский.
Лицения: MIT

🏋️ Детали Тренировки

Датасет: ``
Железо: ОДНА NVIDIA GEFORCE RTX 5060 TI (16GB VRAM)
Эпохи: ...
Шагов: - 10 тысяч
СРЕДНИЙ LOSS: 3.89
Оптимизатор: lr = 2e-4
Контекст: 512 токенов

Downloads last month: 212

Safetensors

Model size

1B params

Tensor type

F32

F16

Dmitriy-Zemskov
/

ViorikaLM-1.2B

🌌 ViorikaLM 1.2B (v1.0)

Подробнее:

⚙️ Детали модели

🏋️ Детали Тренировки

Dataset used to train Dmitriy-Zemskov/ViorikaLM-1.2B