Instructions to use appvoid/linear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/linear with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/linear")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("appvoid/linear", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/linear with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/linear" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/linear", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/linear
- SGLang
How to use appvoid/linear 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 "appvoid/linear" \ --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": "appvoid/linear", "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 "appvoid/linear" \ --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": "appvoid/linear", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/linear with Docker Model Runner:
docker model run hf.co/appvoid/linear
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Download README.md from appvoid/linear: direct link, hf CLI and curl.
- Browser
- Download file 418 Bytes
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https://huggingface.co/appvoid/linear/resolve/a1da170c707db571d44c669ac215ee4895648ab4/README.md
- Command line
-
hf download hf://appvoid/linear@a1da170c707db571d44c669ac215ee4895648ab4/README.md
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curl -L -o README.md https://huggingface.co/appvoid/linear/resolve/a1da170c707db571d44c669ac215ee4895648ab4/README.md
418 Bytes
| language: en | |
| library_name: transformers | |
| tags: | |
| - text-generation | |
| - kronlstm | |
| # void.2 | |
| Private training checkpoints for the 97.9M-parameter KronLSTM model. | |
| Training: 4096-byte byte-level sequences; ClimbMix/DCLM/OpenMathInstruct-2 50/30/20. | |
| Use `latest.json` to locate a full resumable checkpoint containing optimizer masters, AdamW state, RNG state, and stream cursors. FLA/KronLSTM is required for inference. | |