Instructions to use Vikhrmodels/Vikhr-7b-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vikhrmodels/Vikhr-7b-0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Vikhrmodels/Vikhr-7b-0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/Vikhr-7b-0.1")
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7b-0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vikhrmodels/Vikhr-7b-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vikhrmodels/Vikhr-7b-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vikhrmodels/Vikhr-7b-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vikhrmodels/Vikhr-7b-0.1

SGLang

How to use Vikhrmodels/Vikhr-7b-0.1 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 "Vikhrmodels/Vikhr-7b-0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vikhrmodels/Vikhr-7b-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Vikhrmodels/Vikhr-7b-0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vikhrmodels/Vikhr-7b-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vikhrmodels/Vikhr-7b-0.1 with Docker Model Runner:
```
docker model run hf.co/Vikhrmodels/Vikhr-7b-0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Vikhr: модель на базе Mistral для русского языка

Введение в Vikhr: представляем Vikhr, основанную на архитектуре Mistral и предварительно обученную на 400 миллионах токенов в течение трех эпох.

Она должна быть лучше чем оригинальный мистраль на русских задачах, а вот с кодом скорее всего будут проблемы.

Бенчи

Датасет	Vikhr-7b-0.1	Mistral-7B-v0.1
mmlu_ru	0.60	0.66
xwinograd	0.72	0.68
xnli	0.3812	0.3691

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Safetensors

Model size

7B params

Tensor type

F16

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Quantizations

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Vikhr-7b-0.1

Vikhr: модель на базе Mistral для русского языка

Бенчи

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