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---
library_name: transformers
pipeline_tag: image-text-to-text
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- lmms-lab/LLaVA-OneVision-1.5-8B-Instruct
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [lmms-lab/LLaVA-OneVision-1.5-8B-Instruct](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-8B-Instruct).
### Example usage:
```python
from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
from qwen_vl_utils import process_vision_info
import torch
# fix flash_attn_varlen_func, see https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/pull/33/files
from flash_attn.flash_attn_interface import flash_attn_varlen_func
import transformers
transformers.modeling_flash_attention_utils.flash_attn_varlen_func = flash_attn_varlen_func
model_id = "tiny-random/llava-onevision-1.5"
model = AutoModelForCausalLM.from_pretrained(
model_id, dtype=torch.bfloat16, device_map="cuda", trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=32)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
AutoModelForImageTextToText,
set_seed,
)
# fix flash_attn_varlen_func, see https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/pull/33/files
from flash_attn.flash_attn_interface import flash_attn_varlen_func
import transformers
transformers.modeling_flash_attention_utils.flash_attn_varlen_func = flash_attn_varlen_func
source_model_id = "lmms-lab/LLaVA-OneVision-1.5-8B-Instruct"
save_folder = "/tmp/tiny-random/llava-onevision-1.5"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
for k, v in config_json['auto_map'].items():
config_json['auto_map'][k] = f'{source_model_id}--{v}'
config_json['text_config'].update({
'head_dim': 32,
'hidden_size': 8,
'intermediate_size': 64,
'num_hidden_layers': 2,
'num_attention_heads': 8,
'num_key_value_heads': 4,
'layer_types': ['full_attention'] * 2,
'max_window_layers': 2,
})
config_json['vision_config'].update(
{
'depth': 2,
'intermediate_size': 256,
'embed_dim': 32 * 4,
'hidden_size': 32 * 4,
'text_hidden_size': 8,
'num_heads': 4,
'num_hidden_layers': 2,
}
)
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True).to(torch.bfloat16)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
model.generation_config.do_sample = True
print(model.generation_config)
model = model.cpu()
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
def modify_automap(path, source_model_id):
import json
with open(path, 'r', encoding='utf-8') as f:
content = json.load(f)
automap = {}
if content.get('auto_map', None) is not None:
for key, value in content.get('auto_map').items():
if isinstance(value, str):
value = source_model_id + '--' + value.split('--')[-1]
else:
value = [(source_model_id + '--' + v.split('--')[-1]) if '.' in str(v) else v for v in value]
automap[key] = value
with open(path, 'w', encoding='utf-8') as f:
json.dump({**content, 'auto_map': automap}, f, indent=2)
modify_automap(f"{save_folder}/config.json", source_model_id)
# modify_automap(f'{save_folder}/processor_config.json', source_model_id)
# modify_automap(f'{save_folder}/preprocessor_config.json', source_model_id)
# modify_automap(f'{save_folder}/tokenizer_config.json', source_model_id)
for python_file in Path(save_folder).glob('*.py'):
python_file.unlink()
```
### Printing the model:
```text
LLaVAOneVision1_5_ForConditionalGeneration(
(model): LLaVAOneVision1_5_Model(
(visual): RiceTransformerPretrainedModel(
(patch_embed): RicePatchEmbed(
(proj): Conv2d(3, 128, kernel_size=(14, 14), stride=(14, 14), bias=False)
)
(rotary_pos_emb): RiceRotaryEmbedding()
(pre_layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(blocks): ModuleList(
(0-1): 2 x RiceBlock(
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(attn): RiceSdpaAttention(
(qkv): Linear(in_features=128, out_features=384, bias=True)
(proj): Linear(in_features=128, out_features=128, bias=True)
)
(mlp): RiceMlp(
(fc1): Linear(in_features=128, out_features=256, bias=True)
(act): GELUActivation()
(fc2): Linear(in_features=256, out_features=128, bias=True)
)
)
)
(merger): RicePatchMerger(
(ln_q): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=512, out_features=8, bias=True)
)
)
)
(language_model): LLaVAOneVision1_5_TextModel(
(embed_tokens): Embedding(151936, 8)
(layers): ModuleList(
(0-1): 2 x LLaVAOneVision1_5_DecoderLayer(
(self_attn): LLaVAOneVision1_5_SdpaAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): LLaVAOneVision1_5_RMSNorm((32,), eps=1e-06)
(k_norm): LLaVAOneVision1_5_RMSNorm((32,), eps=1e-06)
)
(mlp): LLaVAOneVision1_5_MLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
(input_layernorm): LLaVAOneVision1_5_RMSNorm((8,), eps=1e-06)
(post_attention_layernorm): LLaVAOneVision1_5_RMSNorm((8,), eps=1e-06)
)
)
(norm): LLaVAOneVision1_5_RMSNorm((8,), eps=1e-06)
(rotary_emb): LLaVAOneVision1_5_RotaryEmbedding()
)
)
(lm_head): Linear(in_features=8, out_features=151936, bias=False)
)
``` |