How To Use
from transformers import AutoConfig, AutoProcessor, AutoModelForImageTextToText, PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
import torch
import os
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 = AutoModelForImageTextToText.from_pretrained("KORMo-VLM/KORMo_siglip_stage1", torch_dtype=torch.bfloat16)
proc = AutoProcessor.from_pretrained("KORMo-VLM/KORMo_siglip_stage1")
conversation = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://avatars.githubusercontent.com/u/4470398?v=4"},
{"type": "text", "text": "What does he hold?"},
],
},
]
inputs = proc.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
generate_ids = model.generate(**inputs, max_new_tokens=1024)
output = proc.decode(generate_ids[0])
print(output)
