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Mohammad Ibrahim
commited on
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•
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Parent(s):
9ad123d
initial commit
Browse files- .gitattributes +3 -0
- app.py +127 -0
- model_chkpt/lora_adaptor/.ipynb_checkpoints/adapter_config-checkpoint.json +29 -0
- model_chkpt/lora_adaptor/adapter_config.json +3 -0
- model_chkpt/lora_adaptor/adapter_model.safetensors +3 -0
- model_chkpt/step2_projection.pth +3 -0
- model_chkpt/step2_resblock.pth +3 -0
.gitattributes
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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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/model_chkpt/*.pth filter=lfs diff=lfs merge=lfs -text
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/model_chkpt/lora_adaptor/*.safetensors filter=lfs diff=lfs merge=lfs -text
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/model_chkpt/lora_adaptor/*.json filter=lfs diff=lfs merge=lfs -text
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app.py
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import gradio as gui
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import peft
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from peft import LoraConfig
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from transformers import AutoTokenizer,BitsAndBytesConfig, AutoModelForCausalLM, CLIPVisionModel, AutoProcessor
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import torch
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from peft import PeftModel
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import torch.nn as nn
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import whisper
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import os
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os.environ['https_proxy'] = 'http://185.46.212.90:80'
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os.environ['http_proxy'] = 'http://185.46.212.90:80'
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clip_model_name = "openai/clip-vit-base-patch32"
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phi_model_name = "microsoft/phi-2"
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tokenizer = AutoTokenizer.from_pretrained(phi_model_name, trust_remote_code=True)
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processor = AutoProcessor.from_pretrained(clip_model_name)
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tokenizer.pad_token = tokenizer.eos_token
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IMAGE_TOKEN_ID = 23893 # token for word comment
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QA_TOKEN_ID = 50295 # token for qa
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device = "cuda" if torch.cuda.is_available() else "cpu"
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clip_embed = 768
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phi_embed = 2560
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audio_batch_size = 16
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current_dir = os.getcwd()
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class SimpleResBlock(nn.Module):
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def __init__(self, phi_embed):
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super().__init__()
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self.pre_norm = nn.LayerNorm(phi_embed)
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self.proj = nn.Sequential(
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nn.Linear(phi_embed, phi_embed),
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nn.GELU(),
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nn.Linear(phi_embed, phi_embed)
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)
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def forward(self, x):
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x = self.pre_norm(x)
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return x + self.proj(x)
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# models
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clip_model = CLIPVisionModel.from_pretrained(clip_model_name).to(device)
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projection = torch.nn.Linear(clip_embed, phi_embed).to(device)
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resblock = SimpleResBlock(phi_embed).to(device)
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phi_model = AutoModelForCausalLM.from_pretrained(phi_model_name,trust_remote_code=True).to(device)
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audio_model = whisper.load_model("tiny", device=device)
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lora_adaptor_path = os.path.join(current_dir, 'model_chkpt', 'lora_adaptor')
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projection_path = os.path.join(current_dir, 'model_chkpt', 'step2_projection.pth')
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resblock_path = os.path.join(current_dir, 'model_chkpt', 'step2_resblock.pth')
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# load weights
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model_to_merge = PeftModel.from_pretrained(phi_model,lora_adaptor_path, local_files_only=True, device_map={'': device})
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merged_model = model_to_merge.merge_and_unload()
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projection.load_state_dict(torch.load(projection_path,map_location=torch.device(device)))
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resblock.load_state_dict(torch.load(resblock_path,map_location=torch.device(device)))
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def generate_response(img=None,img_audio=None,val_q=None):
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max_generate_length = 100
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val_combined_embeds = []
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with torch.no_grad():
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# image
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if img is not None:
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image_processed = processor(images=img, return_tensors="pt").to(device)
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clip_val_outputs = clip_model(**image_processed).last_hidden_state[:,1:,:]
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val_image_embeds = projection(clip_val_outputs)
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val_image_embeds = resblock(val_image_embeds).to(torch.float16)
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img_token_tensor = torch.tensor(IMAGE_TOKEN_ID).to(device)
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img_token_embeds = merged_model.model.embed_tokens(img_token_tensor).unsqueeze(0).unsqueeze(0)
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val_combined_embeds.append(val_image_embeds)
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val_combined_embeds.append(img_token_embeds)
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# audio
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if img_audio is not None:
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audio_result = audio_model.transcribe(img_audio)
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audio_text = ''
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for seg in audio_result['segments']:
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audio_text += seg['text']
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audio_text = audio_text.strip()
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audio_tokens = tokenizer(audio_text, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0).to(device)
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audio_embeds = merged_model.model.embed_tokens(audio_tokens).unsqueeze(0)
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val_combined_embeds.append(audio_embeds)
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# text question
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if len(val_q) != 0:
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val_q_tokenised = tokenizer(val_q, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0).to(device)
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val_q_embeds = merged_model.model.embed_tokens(val_q_tokenised).unsqueeze(0)
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val_combined_embeds.append(val_q_embeds)
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if img_audio is not None or len(val_q) != 0: # add QA Token
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QA_token_tensor = torch.tensor(QA_TOKEN_ID).to(device)
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QA_token_embeds = merged_model.model.embed_tokens(QA_token_tensor).unsqueeze(0).unsqueeze(0)
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val_combined_embeds.append(QA_token_embeds)
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val_combined_embeds = torch.cat(val_combined_embeds,dim=1)
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predicted_caption = merged_model.generate(inputs_embeds=val_combined_embeds,
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max_new_tokens=max_generate_length,
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return_dict_in_generate = True)
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predicted_captions_decoded = tokenizer.batch_decode(predicted_caption.sequences[:, 1:])[0]
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predicted_captions_decoded = predicted_captions_decoded.replace("<|endoftext|>", "")
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return predicted_captions_decoded
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# Gradio interface setup with added styling
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with gui.Blocks() as app_interface:
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with gui.Row():
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with gui.Column():
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image_input = gui.Image(label='Upload Image', type="pil")
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with gui.Column():
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audio_input = gui.Audio(label="Audio Input", sources=['microphone', 'upload'], type='filepath')
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text_input = gui.Text(label='Enter Text', placeholder="Type your query here...")
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with gui.Row():
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output_response = gui.Textbox(label='Generated Response', placeholder="Response will appear here...", lines=5)
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submit_button = gui.Button("Generate Response", variant="primary")
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submit_button.click(generate_response, inputs=[image_input, audio_input, text_input], outputs=output_response)
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if __name__ == "__main__":
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app_interface.launch(share=True)
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model_chkpt/lora_adaptor/.ipynb_checkpoints/adapter_config-checkpoint.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "microsoft/phi-2",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0.1,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 64,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"fc2",
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"v_proj",
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"fc1",
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"k_proj",
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"q_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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model_chkpt/lora_adaptor/adapter_config.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:23212cff0181dce73efde3004af2309e9ad4a13ace72aa36d3d236874d85b8e4
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size 603
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model_chkpt/lora_adaptor/adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5ed4cfe545b28f8effe9ded98b472502a93a5e5e42edd6384591f0e1d71c3770
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size 335586800
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model_chkpt/step2_projection.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:6ec5d298b71c4b50f2b626e6df9a73d02d012da0794c1b768610fe52f4a8f860
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size 7876174
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model_chkpt/step2_resblock.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:c161997824ad80b1af83639cd051a8beda84d4967be50982821249c509fab62c
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size 52472590
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