tiny random hunyuan v1 dense EU AI Act Compliance Profile

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))

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