Instructions to use ClosRise/RandomNandi_200m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClosRise/RandomNandi_200m with Transformers:

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

pipe = pipeline("text-generation", model="ClosRise/RandomNandi_200m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ClosRise/RandomNandi_200m")
model = AutoModelForMultimodalLM.from_pretrained("ClosRise/RandomNandi_200m")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClosRise/RandomNandi_200m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClosRise/RandomNandi_200m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClosRise/RandomNandi_200m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ClosRise/RandomNandi_200m

SGLang

How to use ClosRise/RandomNandi_200m 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 "ClosRise/RandomNandi_200m" \
    --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": "ClosRise/RandomNandi_200m",
		"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 "ClosRise/RandomNandi_200m" \
        --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": "ClosRise/RandomNandi_200m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ClosRise/RandomNandi_200m with Docker Model Runner:
```
docker model run hf.co/ClosRise/RandomNandi_200m
```

Model Card for Model ID

Не является оригинальной моделью , для создания модели была взята модель Rta-AILabs/Nandi-Mini-150M и методом расширения слоев были созданы новые, точнее на основе llama-подобное архитектуры перенесли знания с Nandi на нее + дополнили кол-во слоев. :3

Стоит отметить что модель толком не обучалась после переноса, так что готовая версия на данной странице является "рандомной" по своей сути.

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Safetensors

Model size

0.2B params

Tensor type

F32

Collection including ClosRise/RandomNandi_200m

Nandi Experiments

Collection

Эксперименты над индийской серией моделей nandi • 2 items • Updated Apr 21