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: 1,785 Bytes
b3dd6bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"allow_neg_eigval": false,
"architectures": [
"KDAForCausalLM"
],
"attn": null,
"attn_mode": "chunk",
"attnres_block_size": null,
"bos_token_id": 1,
"conv_size": 4,
"disable_recompute": true,
"dtype": "float32",
"eos_token_id": 2,
"expand_v": 2,
"fuse_cross_entropy": true,
"fuse_linear_cross_entropy": false,
"fuse_norm": true,
"fuse_swiglu": true,
"head_dim": 64,
"head_init_design": null,
"hidden_act": "swish",
"hidden_ratio": 4,
"hidden_size": 512,
"initialization_manifest": {
"A_log_rule": "GDN v3_random_a source A_log",
"design_note": "KDA aligned random repeat: GDN v3_random_a head-level A_log/dtbias, per-head channel dtbias centered noise std=0.03",
"dt_bias_rule": "GDN-aligned head means with deterministic small channel noise; channel_std=0.03",
"experiment": "v4_random_c",
"gdn_source_csv": "/root/checkpoints/gdn/training/gdn100m-v3-random-5b-v3_random_a/initial_head_params.csv",
"repeat_of": "init_v4_random_a",
"repeat_rule": "identical initialization and training configuration to the source run; weights copied verbatim so run-to-run spread reflects training non-determinism only",
"source_package": "/root/checkpoints/kda/evals/_init_v4_random_a",
"source_weights_sha256": "f4333d94db0e652c24de2392ff77a3ba98064b3cbe7773fbb71d17edd64a9153"
},
"initializer_range": 0.02,
"intermediate_size": null,
"lower_bound": null,
"max_position_embeddings": 4096,
"model_type": "kda",
"norm_eps": 1e-06,
"num_heads": 6,
"num_hidden_layers": 18,
"num_v_heads": 6,
"pad_token_id": 0,
"safe_gate": false,
"tie_word_embeddings": false,
"transformers_version": "5.9.0",
"use_cache": true,
"use_l2warp": false,
"use_short_conv": true,
"vocab_size": 32000
}
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