Instructions to use ClosRise/RandomNandi_200m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ClosRise/RandomNandi_200m") model = AutoModelForCausalLM.from_pretrained("ClosRise/RandomNandi_200m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
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library_name: transformers
language:
- hi
- en
---
# 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. |