Instructions to use Appodoom/ldm-with-subd-m2-3-6pct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Appodoom/ldm-with-subd-m2-3-6pct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Appodoom/ldm-with-subd-m2-3-6pct", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Appodoom/ldm-with-subd-m2-3-6pct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
LDM with subdivision, M2 at 3.6% training size
This is the M2 checkpoint selected by saved validation loss at the 3.6% training size in the LDM capacity experiment. It uses 4 layers, embedding width 192, 4 attention heads, and a 6,656-token context. Its normal training subset contains 9,830 cycles (16.15 hours). The exported checkpoint is from step 20,000.
Held-out test results
The saved normal test split contains 17 files, 34,184 cycles, and 56.10 hours. Evaluation used the archived LDM scorer on an NVIDIA A100 SXM 80 GB.
| Metric | Value |
|---|---|
| Exact HIT accuracy, including silence | 71.471% |
| Overall token accuracy | 81.003% |
| Skeleton HIT accuracy | 94.133% |
| Test cross-entropy | 0.395484 |
| Saved checkpoint validation cross-entropy | 0.395874 |
Usage
This snapshot uses atomic rhythm tokens and five-line .derbake files.
It was built from a selected training checkpoint. trust_remote_code=True
executes the model/processor code from the selected repository revision.
from transformers import AutoModel, AutoProcessor
repo_id = "Appodoom/ldm-with-subd-m2-3-6pct"
revision = "main" # Replace with the verified release commit SHA for reproducibility.
processor = AutoProcessor.from_pretrained(repo_id, revision=revision, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, revision=revision, trust_remote_code=True).eval()
inputs = processor(derbake_path="prompt.derbake", return_tensors="pt")
ids = model.generate(input_ids=inputs["input_ids"].to(next(model.parameters()).device),
num_cycles=10, max_new_tokens=4096)
rhythm = processor.decode_generation(ids[0], expected_num_cycles=10)
processor.save_derbake(rhythm, "generated.derbake")
The call returns only newly accepted cycles. Invalid sampled cycles are skipped
with a Python warning; use return_generation_report=True for details. A token
cap reached before ten valid cycles raises an error and produces no output file.
The writer uses 120 BPM, amplitude 1.5, and a blank skeleton line. The model
supports batch size one and at most 16 beats per cycle.
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