Update README.md

README.md

@@ -1,3 +1,60 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+import torch
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+import os
+
+# Create a tiny config for testing
+from transformers.models.hunyuan_v1_dense.configuration_hunyuan_v1_dense import HunYuanDenseV1Config
+
+tiny_config = HunYuanDenseV1Config(
+    vocab_size=300,
+    hidden_size=64,
+    intermediate_size=128,
+    num_hidden_layers=2,
+    num_attention_heads=4,
+    head_dim=16,
+    num_key_value_heads=2,
+    hidden_act="silu",
+    max_position_embeddings=128,
+    rms_norm_eps=1e-05,
+    use_cache=True,
+    tie_word_embeddings=False,
+    rope_theta=10000.0,
+    attention_bias=False,
+    attention_dropout=0.0,
+    use_qk_norm=True,
+    bos_token_id=1,
+    eos_token_id=2,
+    pad_token_id=0,
+)
+
+print("Config created:", tiny_config.model_type)
+
+# Create model from config
+model = AutoModelForCausalLM.from_config(tiny_config)
+model.eval()
+print("Model created, params:", sum(p.numel() for p in model.parameters()))
+
+# Save model
+save_dir = "/home/panas/git/optimum-intel/tiny-random-hunyuan-v1-dense"
+model.save_pretrained(save_dir)
+tiny_config.save_pretrained(save_dir)
+
+# Create a simple tokenizer config for testing
+from transformers import PreTrainedTokenizerFast
+tokenizer = PreTrainedTokenizerFast(
+    tokenizer_object=None,
+    bos_token="<s>",
+    eos_token="</s>",
+    unk_token="<unk>",
+    pad_token="<pad>",
+)
+# Just save a minimal tokenizer
+# Actually, for tests, we can use the AutoTokenizer approach or skip tokenizer
+
+print(f"Saved tiny model to {save_dir}")
+print("Files:", os.listdir(save_dir))
+