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
Download checkpoints/main_kda_random/model.safetensors from SpeedAI-Lab/DAMI: direct link, hf CLI and curl.
- Browser
- Download file 407 MB
-
https://huggingface.co/SpeedAI-Lab/DAMI/resolve/main/checkpoints/main_kda_random/model.safetensors
- Command line
-
hf download hf://SpeedAI-Lab/DAMI/checkpoints/main_kda_random/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/SpeedAI-Lab/DAMI/resolve/main/checkpoints/main_kda_random/model.safetensors
407 MB
- Xet hash:
- f6f7aed3602839481ab4dca13fd9e5758e4c4706c88441a07a804334167ae529
- Size of remote file:
- 407 MB
- SHA256:
- f6033721f703a952664942d1a51e7704d45688fba56ae432d5fd656a9ea1b6c1
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