Instructions to use Vikhrmodels/Mini-cpm-vikhr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vikhrmodels/Mini-cpm-vikhr with Transformers:

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

pipe = pipeline("text-generation", model="Vikhrmodels/Mini-cpm-vikhr", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Mini-cpm-vikhr", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vikhrmodels/Mini-cpm-vikhr with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Vikhrmodels/Mini-cpm-vikhr

SGLang

How to use Vikhrmodels/Mini-cpm-vikhr 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/Mini-cpm-vikhr" \
    --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/Mini-cpm-vikhr",
		"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/Mini-cpm-vikhr" \
        --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/Mini-cpm-vikhr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Task	Version	Metric	Value		Stderr
parus	0	acc	0.4825	±	0.0250
rcb	0	acc	0.3333	±	0.0226
		f1_macro	0.1675
rwsd	0	acc	0.4802	±	0.0203
mmlu	0	acc	0.3402	±	похуй+похуй я выполнил домашку, а cбер девайсес mmlu нет, блять какой же еБаный код ебал рот блять нахуй я этим занимаюсь сука???

https://wandb.ai/alexwortega/cpm_rus/runs/pzj0wcui

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for Vikhrmodels/Mini-cpm-vikhr

Quantizations

2 models

Vikhrmodels
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Mini-cpm-vikhr

Model tree for Vikhrmodels/Mini-cpm-vikhr

Dataset used to train Vikhrmodels/Mini-cpm-vikhr