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spb/forge Public MIT

Forge — LLM training from scratch in pure C++20 + Metal on Apple Silicon.

C++ 61.2% C 23% Python 7.6% TeX 7.2% CMake 1.1%
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1{2 "config": {3  "_comment": "~52M params, sized between gpt-25m and gpt-100m. vocab 4096 to match the local tok4096 train.bin (19.14M tokens). batch_size is the MICRO-batch: 8 x 8 x 1024 = 65536 tokens/step; 1170 steps = ~4 epochs over the 19.14M-token set. Warmup is 10% of the run. precision is parsed but not yet honored - all kernels are f32.",4  "model": {5   "activation": "swiglu",6   "context_length": 1024,7   "d_ff": 1728,8   "d_model": 640,9   "dropout": 0.0,10   "n_heads": 10,11   "n_kv_heads": 10,12   "n_layers": 10,13   "name": "gpt-50m",14   "norm": "rmsnorm",15   "norm_eps": 1e-06,16   "rope_theta": 10000.0,17   "tied_embeddings": true,18   "use_rope": true,19   "vocab_size": 409620  },21  "train": {22   "batch_size": 8,23   "beta1": 0.9,24   "beta2": 0.95,25   "checkpoint_every": 200,26   "eps": 1e-08,27   "eval_batches": 20,28   "eval_every": 100,29   "grad_accum_steps": 8,30   "grad_clip": 1.0,31   "lr": 0.0005,32   "max_steps": 1170,33   "min_lr_ratio": 0.1,34   "precision": "f32",35   "seed": 1337,36   "warmup_steps": 117,37   "weight_decay": 0.138  }39 },40 "dtype": "f32",41 "fmodel": 1,42 "parent": "",43 "self": "manifest-000000.json",44 "step": 1170,45 "tag": "one-epoch-x4",46 "tensors": {47  "blocks.0.attn.wk.weight": 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