初始化项目，由ModelHub XC社区提供模型

Model: timaeus/dodecahedron-67m
Source: Original Platform

README.md

@@ -0,0 +1,68 @@
+---
+language: en
+license: apache-2.0
+tags:
+  - language-model
+  - slt
+  - singular-learning-theory
+  - patterning
+  - timaeus
+datasets:
+  - HuggingFaceFW/fineweb
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Dodecahedron-67M
+
+A 67M parameter language model from the Dodecahedron family, trained for singular learning theory (SLT) research.
+
+## Model Details
+
+| Parameter | Value |
+|-----------|-------|
+| Parameters | 67M |
+| Architecture | LlamaForCausalLM |
+| Hidden size | 512 |
+| Layers | 20 |
+| Attention heads | 8 (4 KV heads, GQA 2:1) |
+| Head dim | 64 |
+| MLP intermediate | 1408 |
+| Vocab size | 16,384 |
+| Context length | 2,048 |
+| Tied embeddings | Yes |
+
+## Training
+
+- **Data**: 40B tokens of [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
+- **Tokenizer**: Custom 16k vocab BPE trained on FineWeb (same as Dodecahedron-32M)
+- **Optimizer**: AdamW (betas 0.9/0.95, weight decay 0.01, grad clip 1.0)
+- **Learning rate**: 3.5e-3 with cosine decay, 1000 warmup steps
+- **Batch size**: 524k tokens/step (64 micro × 4 grad accum × 2048 seq)
+- **Precision**: bfloat16
+- **Hardware**: 8× H100
+
+## Checkpoints
+
+53 checkpoints are available as branches, log-spaced from step 1 to step 9536 (~40B tokens):
+
+```python
+from transformers import AutoModelForCausalLM
+
+# Final checkpoint
+model = AutoModelForCausalLM.from_pretrained("timaeus/dodecahedron-67m-v2")
+
+# Intermediate checkpoint
+model = AutoModelForCausalLM.from_pretrained("timaeus/dodecahedron-67m-v2", revision="step5108")
+```
+
+Available steps: 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 19, 22, 25, 30, 35, 40, 47, 55, 65, 75, 88, 103, 121, 141, 165, 193, 225, 263, 308, 359, 420, 491, 574, 671, 785, 917, 1072, 1253, 1465, 1713, 2002, 2340, 2735, 3198, 3738, 4369, 5108, 5970, 6979, 8158, 9536
+
+## Purpose
+
+The Dodecahedron family is designed as small, well-characterized reference models for developmental interpretability and SLT research. Having densely-checkpointed models enables studying learning dynamics, phase transitions, and the geometry of the loss landscape throughout training.
+
+## Related
+
+- [Dodecahedron-32M](https://huggingface.co/timaeus/dodecahedron-32m-v2) — same architecture scaled down
+- [Timaeus](https://timaeus.co) — research organization