KORMo-VLM/KORMo_VLM_stage2_nohead_35per

import os
import torch
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForImageTextToText, AutoConfig, set_seed
from transformers import PretrainedConfig, AutoModelForVision2Seq, AutoProcessor,AutoModelForCausalLM, AutoModel
from transformers.modeling_rope_utils import rope_config_validation
import deepspeed
import torch
from transformers import AutoConfig
from peft import PeftModel

class KORMoConfig(PretrainedConfig):
    model_type = "kormo"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }

    def __init__(
        self,
        vocab_size=112576,
        hidden_size=6144,
        intermediate_size=21504,
        num_hidden_layers=48,
        num_attention_heads=40,
        num_key_value_heads=8,
        hidden_act="silu",
        max_position_embeddings=131072,
        initializer_range=0.02,
        rms_norm_eps=1e-05,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=0,
        eos_token_id=1,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_theta=500000.0,
        attention_bias=False,
        attention_dropout=0.0,
        rope_scaling=None,
        mlp_bias=False,
        head_dim=128,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
        self.mask_type = None
        
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self)

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
        
AutoConfig.register("kormo", KORMoConfig)

model = AutoModelForVision2Seq.from_pretrained("KORMo-VLM/KORMo_VLM_stage1_nohead",device_map='cpu',dtype='auto')
lora_model = PeftModel.from_pretrained(model,"KORMo-VLM/KORMo_VLM_stage2_nohead_35per")