Text Generation
Transformers
Safetensors
Russian
fly
connectome
reservoir-computing
echo-state-network
fruit-fly
drosophila
malecns
russian
custom_code
Instructions to use igorktech/nanofly-decoder-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igorktech/nanofly-decoder-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="igorktech/nanofly-decoder-ru", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("igorktech/nanofly-decoder-ru", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use igorktech/nanofly-decoder-ru with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "igorktech/nanofly-decoder-ru" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "igorktech/nanofly-decoder-ru", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/igorktech/nanofly-decoder-ru
- SGLang
How to use igorktech/nanofly-decoder-ru 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 "igorktech/nanofly-decoder-ru" \ --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": "igorktech/nanofly-decoder-ru", "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 "igorktech/nanofly-decoder-ru" \ --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": "igorktech/nanofly-decoder-ru", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use igorktech/nanofly-decoder-ru with Docker Model Runner:
docker model run hf.co/igorktech/nanofly-decoder-ru
Download connectome.png from igorktech/nanofly-decoder-ru: direct link, hf CLI and curl.
- Browser
- Download file 203 kB
-
https://huggingface.co/igorktech/nanofly-decoder-ru/resolve/main/connectome.png
- Command line
-
hf download hf://igorktech/nanofly-decoder-ru/connectome.png
-
curl -L -o connectome.png https://huggingface.co/igorktech/nanofly-decoder-ru/resolve/main/connectome.png
203 kB

- Xet hash:
- 75af7c87fcbd9ed630a19f58b3435090d91bda64a0d58a058c7099ab87441775
- Size of remote file:
- 203 kB
- SHA256:
- fa83276e2e73017335ecb6e7adb66f26ed0439b46757ad3bcd82d9979b86acd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.