Update README.md

README.md

@@ -223,71 +223,8 @@ model-index:
 
 ## Usage
 
-This repo contains the **EMOVA-Qwen2.5-7B** checkpoint organized in the **HuggingFace format**, and thus, and be directly loaded with **transformers Auto APIs**.
+This repo contains the **EMOVA-Qwen2.5-7B** checkpoint organized in the **original format** of our [EMOVA codebase](https://github.com/emova-ollm/EMOVA), and thus, it should be utilized together with EMOVA codebase. Its paired config file is provided [here](https://github.com/emova-ollm/EMOVA/blob/main/configs/example/emova/qwen2_5_qwen2vit_nativeAnyres_7b/2.finetune.py). Check [here](https://github.com/emova-ollm/EMOVA#gradio-web-demo) to launch a web demo using this checkpoint.
 
-```python
-from transformers import AutoModel, AutoProcessor
-from PIL import Image
-import torch
-
-### Uncomment if you want to use Ascend NPUs
-# import torch_npu
-# from torch_npu.contrib import transfer_to_npu
-
-# prepare models and processors
-model = AutoModel.from_pretrained(
-    "Emova-ollm/emova-qwen-2-5-7b-hf",
-    torch_dtype=torch.bfloat16,
-    attn_implementation='flash_attention_2', # OR 'sdpa' for Ascend NPUs
-    low_cpu_mem_usage=True,
-    trust_remote_code=True).eval().cuda()
-processor = AutoProcessor.from_pretrained("Emova-ollm/emova-qwen-2-5-7b-hf", trust_remote_code=True)
-
-# only necessary for spoken dialogue
-# Note to inference with speech inputs/outputs, **emova_speech_tokenizer** is still a necessary dependency (https://huggingface.co/Emova-ollm/emova_speech_tokenizer_hf#install).
-speeck_tokenizer = AutoModel.from_pretrained("Emova-ollm/emova_speech_tokenizer_hf", torch_dtype=torch.float32, trust_remote_code=True).eval().cuda()
-processor.set_speech_tokenizer(speeck_tokenizer)
-
-# Example 1: image-text
-inputs = dict(
-    text=[
-        {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
-        {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What's shown in this image?"}]},
-        {"role": "assistant", "content": [{"type": "text", "text": "This image shows a red stop sign."}]},
-        {"role": "user", "content": [{"type": "text", "text": "Describe the image in more details."}]},
-    ],
-    images=Image.open('path/to/image')
-)
-
-# Example 2: text-audio
-inputs = dict(
-    text=[{"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}],
-    audios='path/to/audio'
-)
-
-# Example 3: image-text-audio
-inputs = dict(
-    text=[{"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}],
-    images=Image.open('path/to/image'),
-    audios='path/to/audio'
-)
-
-# run processors
-has_speech = 'audios' in inputs.keys()
-inputs = processor(**inputs, return_tensors="pt")
-inputs = inputs.to(model.device)
-
-# prepare generation arguments
-gen_kwargs = {"max_new_tokens": 4096, "do_sample": False} # add if necessary
-speech_kwargs = {"speaker": "female", "output_wav_prefix": "output"} if has_speech else {}
-
-# run generation
-# for speech outputs, we will return the saved wav paths (c.f., output_wav_prefix)
-with torch.no_grad():
-    outputs = model.generate(**inputs, **gen_kwargs)
-    outputs = outputs[:, inputs['input_ids'].shape[1]:]
-    print(processor.batch_decode(outputs, skip_special_tokens=True, **speech_kwargs))
-```
 
 ## Citation