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wehub-resource-sync a203934033
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chore: import upstream snapshot with attribution
2026-07-13 13:34:58 +08:00

81 lines
3.3 KiB
Python

# Copyright (c) ModelScope Contributors. All rights reserved.
import sys
from functools import wraps
from transformers import PreTrainedModel
from typing import Any, Dict
from swift.template import TemplateType
from swift.utils import git_clone_github, safe_snapshot_download
from ..constant import MLLMModelType
from ..model_arch import ModelArch
from ..model_meta import Model, ModelGroup, ModelMeta
from ..register import ModelLoader, register_model
class ValleyLoader(ModelLoader):
def get_config(self, model_dir: str):
local_repo_path = self.local_repo_path
if not local_repo_path:
repo_path = 'https://github.com/bytedance/Valley.git'
local_repo_path = git_clone_github(repo_path)
sys.path.append(local_repo_path)
from valley_eagle.model.language_model.valley_qwen2 import ValleyConfig
self.auto_config_cls = ValleyConfig
return super().get_config(model_dir)
def get_model(self, model_dir: str, config, processor, model_kwargs) -> PreTrainedModel:
from transformers.modeling_outputs import CausalLMOutputWithPast
from valley_eagle.model.language_model.valley_qwen2 import ValleyQwen2ForCausalLM
config.mm_vision_tower = safe_snapshot_download('AI-ModelScope/siglip-so400m-patch14-384', check_local=True)
config.eagle_vision_tower = safe_snapshot_download('Qwen/Qwen2-VL-7B-Instruct', check_local=True)
auto_model_cls = ValleyQwen2ForCausalLM
if not hasattr(ValleyQwen2ForCausalLM, '_origin_forward'):
forward = ValleyQwen2ForCausalLM.forward
ValleyQwen2ForCausalLM._origin_forward = forward
@wraps(forward)
def new_forward(*args, **kwargs):
import torch
outputs = forward(*args, **kwargs)
loss = outputs.loss
if loss is not None and loss.shape[-1] > 0:
loss = torch.mean(loss, dim=-1)
return CausalLMOutputWithPast(
loss=loss,
logits=outputs.logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
ValleyQwen2ForCausalLM.forward = new_forward
self.auto_model_cls = auto_model_cls
model = super().get_model(model_dir, config, processor, model_kwargs)
model.generation_config.repetition_penalty = 1.0 # Otherwise, Error. Same for original code.
from transformers import AutoProcessor, SiglipImageProcessor
processor.image_processor = SiglipImageProcessor.from_pretrained(model.config.mm_vision_tower)
processor.qwen2vl_processor = AutoProcessor.from_pretrained(
model.config.eagle_vision_tower, max_pixels=1280 * 28 * 28)
processor.image_processor.crop_size = processor.image_processor.size['height']
return model
register_model(
ModelMeta(
MLLMModelType.valley,
[
ModelGroup([
Model('bytedance-research/Valley-Eagle-7B'),
], ),
],
ValleyLoader,
template=TemplateType.valley,
architectures=['ValleyQwen2ForCausalLM'],
model_arch=ModelArch.valley,
requires=['transformers>=4.42', 'av'],
tags=['vision'],
))