Text Generation
Transformers
Safetensors
English
dami
linear-attention
long-context
initialization
ruler
Instructions to use SpeedAI-Lab/DAMI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpeedAI-Lab/DAMI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SpeedAI-Lab/DAMI")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpeedAI-Lab/DAMI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SpeedAI-Lab/DAMI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SpeedAI-Lab/DAMI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SpeedAI-Lab/DAMI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SpeedAI-Lab/DAMI
- SGLang
How to use SpeedAI-Lab/DAMI 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 "SpeedAI-Lab/DAMI" \ --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": "SpeedAI-Lab/DAMI", "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 "SpeedAI-Lab/DAMI" \ --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": "SpeedAI-Lab/DAMI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SpeedAI-Lab/DAMI with Docker Model Runner:
docker model run hf.co/SpeedAI-Lab/DAMI
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Download badges/github.svg from SpeedAI-Lab/DAMI: direct link, hf CLI and curl.
- Browser
- Download file 677 Bytes
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https://huggingface.co/SpeedAI-Lab/DAMI/resolve/main/badges/github.svg
- Command line
-
hf download hf://SpeedAI-Lab/DAMI/badges/github.svg
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curl -L -o github.svg https://huggingface.co/SpeedAI-Lab/DAMI/resolve/main/badges/github.svg
677 Bytes
| <svg xmlns="http://www.w3.org/2000/svg" width="112" height="24" role="img" aria-label="GitHub: Code"> | |
| <title>GitHub: Code</title> | |
| <linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#fff" stop-opacity=".12"/><stop offset="1" stop-opacity=".12"/></linearGradient> | |
| <clipPath id="r"><rect width="112" height="24" rx="4"/></clipPath> | |
| <g clip-path="url(#r)"><path fill="#555" d="M0 0h60v24H0z"/><path fill="#24292f" d="M60 0h52v24H60z"/><rect width="112" height="24" fill="url(#s)"/></g> | |
| <g fill="#fff" font-family="Verdana,DejaVu Sans,sans-serif" text-anchor="middle" font-size="12"><text x="30" y="16">GitHub</text><text x="86" y="16">Code</text></g> | |
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