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
File size: 1,205 Bytes
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"A_init_range": [
1,
16
],
"D_has_hdim": false,
"architectures": [
"Mamba2ForCausalLM"
],
"bos_token_id": 1,
"chunk_size": 256,
"conv_init": null,
"conv_kernel": 4,
"dt_init_floor": 0.0001,
"dt_limit": [
0.0,
{
"__float__": "Infinity"
}
],
"dt_max": 0.1,
"dt_min": 0.001,
"dtype": "bfloat16",
"eos_token_id": 2,
"expand": 2,
"fuse_cross_entropy": true,
"fuse_linear_cross_entropy": false,
"fuse_norm": true,
"head_dim": 64,
"head_init_design": null,
"hidden_act": "silu",
"hidden_size": 512,
"initializer_range": 0.02,
"max_position_embeddings": 4096,
"model_type": "mamba2",
"n_groups": 1,
"norm_before_gate": false,
"norm_eps": 1e-05,
"num_heads": 16,
"num_hidden_layers": 42,
"pad_token_id": 0,
"rescale_prenorm_residual": true,
"residual_in_fp32": true,
"rms_norm": true,
"rmsnorm": true,
"state_size": 128,
"tie_word_embeddings": false,
"time_step_floor": 0.0001,
"time_step_max": 0.1,
"time_step_min": 0.001,
"time_step_rank": 128,
"transformers_version": "5.9.0",
"use_bias": false,
"use_cache": true,
"use_conv_bias": true,
"use_l2warp": false,
"vocab_size": 32000
}
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