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MonumentalSystems
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
MonumentalSystems
Model Tree
Hcompany/Holo-3.1-0.8B
Input Modalities
Output Modalities
Supported Functionality
target_modules = ["k_proj", "v_proj", "o_proj"] (q_proj is gated on Holo; dense-FFN delta not yet wired in Atlas v0)layers_to_transform = [3, 7, 11, 15, 19, 23] — the 6 full-attention layersr = 32, lora_alpha = 64 (scale = α/r = 2.0), use_rslora = false, bias = "none"peft + transformers, 18 epochs on 6 overfit Q→A pairs, r=32, lora_alpha=64,
lr 2e-4, bf16, on a single NVIDIA GB10 (Grace-Blackwell, aarch64, CUDA 13). Loss → 0.0008.
A demo artifact, not a general-purpose fine-tune.
bash
# serve with adapter #1 resident in the single slot, rotation armed:ATLAS_LORA_ROTATE=1 spark serve Hcompany/Holo-3.1-0.8B \--lora-adapter starfall=MonumentalSystems/Holo-3.1-0.8B-lora-demo \--max-loras 1 --max-lora-rank 64# → "launch codeword?" answers STARFALL-4728# swap THIS adapter into the single slot at runtime:curl -XPOST localhost:8000/v1/lora/load -d '{"name":"vega","path":"/path/to/this-adapter","slot":0}'# → "launch codeword?" now answers MOONVEIL-3390