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train.py

@@ -47,9 +47,10 @@ n_epoch = 100                                        # the 'epoch' here is actua
 
 model_level = 'character' # 'character' (recommended) or 'word'
 
-ctx_len = 256 # context length
-n_layer = 5
-n_head = 8
+ctx_len = 256 # context length, try 512 or 1024 if you have good GPU
+n_layer = 5   # try 12 for 100M, 24 for 300M
+n_head = 8    # try 12 for 100M, 16 for 300M
+
 n_embd = n_head * 64
 n_attn = n_embd
 n_ffn = n_embd
@@ -65,7 +66,7 @@ epoch_length_fixed = 10000                          # make an 'epoch' very short
 
 ######## special hyperparameters for RWKV model ########
 rwkv_emb_scale = 0.4                                # scale of initial embedding. 0.4 is a good choice
-rwkv_tiny_attn = 64 if (datafile_type == 0 and ctx_len > 600) else 0 # extra tiny attention dim, useful for long ctx char-level english
+rwkv_tiny_attn = 0#64 if (datafile_type == 0 and ctx_len > 600) else 0 # extra tiny attention dim, useful for long ctx char-level english
 rwkv_tiny_head = 1                                  # 1 is good enough. 8 is slow
 # n_side_proj = 512                                 # extra 'side projection', quite useful for BPE models 
 
@@ -127,6 +128,9 @@ model = GPT(GPTConfig(train_dataset.vocab_size, train_dataset.ctx_len, model_typ
                 rwkv_emb_scale=rwkv_emb_scale, rwkv_tiny_attn=rwkv_tiny_attn, rwkv_tiny_head=rwkv_tiny_head,
                 n_layer=n_layer, n_head=n_head, n_embd=n_embd, n_attn=n_attn, n_ffn=n_ffn))
 
+# load a trained model
+# model.load_state_dict(torch.load('trained-xxx.pth').state_dict())
+
 print('model', model_type, 'epoch', n_epoch, 'batchsz', batch_size, 'betas', betas, 'eps', eps, 'wd', weight_decay, 'ctx', ctx_len, 'layer', n_layer, 'head', n_head, 'embd', n_embd, 'attn', n_attn, 'ffn', n_ffn)
 tconf = TrainerConfig(model_type=model_type, max_epochs=n_epoch, batch_size=batch_size, weight_decay=weight_decay,
                         learning_rate=lr_init, lr_decay=True, lr_final=lr_final, betas=betas, eps=eps,