Text Generation
PyTorch
English
MultiXiPARFLM
research
semsimula
conservative-language-model
scalar-potential
lagrangian-mechanics
energy-based-model
physics-informed
parflm
parf
multi-channel-xi
sparse-routing
tinystories
non-transformer
attention-free
constant-memory-inference
riemannian-geometry
riemannian-geodesics
damped-riemannian-geometry
Eval Results (legacy)
| { | |
| "model_type": "MultiXiPARFLM", | |
| "model_family": "semsimula-splm", | |
| "vocab_size": 50257, | |
| "d": 256, | |
| "n_layers": 8, | |
| "v_hidden": 1024, | |
| "v_depth": 3, | |
| "max_len": 1024, | |
| "block_size": 512, | |
| "gamma": 0.30, | |
| "xi_channels": 8, | |
| "xi_alpha_inits": "log_spaced", | |
| "v_phi_kind": "structural_competitive", | |
| "v_phi_hidden": 128, | |
| "top_k": 8, | |
| "gumbel_tau_start": 1.0, | |
| "gumbel_tau_end": 0.1, | |
| "mass_model": "logfreq", | |
| "integrator": "semi_implicit_euler", | |
| "ln_after_step": true, | |
| "use_gathered_v_phi": true, | |
| "use_layer_checkpoint": true, | |
| "total_parameters": 17632215, | |
| "best_val_ppl": 12.06, | |
| "training_steps": 8000, | |
| "dataset": "roneneldan/TinyStories" | |
| } | |