Update README.md

README.md

@@ -202,108 +202,6 @@ for t in range(31):
     t += 1
 ```
 
-Here we show a code snippet to show you how to do **common video (multi-turn) qa** with `transformers` and the above utils:
-```python
-import functools, torch
-from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl
-apply_liger_kernel_to_qwen2_vl() # important. our model is trained with this. keep consistency
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, LogitsProcessor, logging
-from livecc_utils import prepare_multiturn_multimodal_inputs_for_generation, get_smart_resized_clip, get_smart_resized_video_reader
-from qwen_vl_utils import process_vision_info
-
-class LiveCCDemoInfer:
-  fps = 2
-  initial_fps_frames = 6
-  streaming_fps_frames = 2
-  initial_time_interval = initial_fps_frames / fps
-  streaming_time_interval = streaming_fps_frames / fps
-  frame_time_interval = 1 / fps
-
-  def __init__(self, model_path: str = None, device: str = 'cuda'):
-      self.model = Qwen2VLForConditionalGeneration.from_pretrained(
-          model_path, torch_dtype="auto", 
-          device_map=device, 
-          attn_implementation='flash_attention_2'
-      )
-      self.processor = AutoProcessor.from_pretrained(model_path, use_fast=False)
-      self.streaming_eos_token_id = self.processor.tokenizer(' ...').input_ids[-1]
-      self.model.prepare_inputs_for_generation = functools.partial(prepare_multiturn_multimodal_inputs_for_generation, self.model)
-      message = {
-          "role": "user",
-          "content": [
-              {"type": "text", "text": 'livecc'},
-          ]
-      }
-      texts = self.processor.apply_chat_template([message], tokenize=False)
-      self.system_prompt_offset = texts.index('<|im_start|>user')
-
-  def video_qa(
-      self,
-      message: str,
-      state: dict,
-      do_sample: bool = False,
-      repetition_penalty: float = 1.05,
-      **kwargs,
-  ): 
-      """
-      state: dict, (maybe) with keys:
-          video_path: str, video path
-          video_timestamp: float, current video timestamp
-          last_timestamp: float, last processed video timestamp
-          last_video_pts_index: int, last processed video frame index
-          video_pts: np.ndarray, video pts
-          last_history: list, last processed history
-          past_key_values: llm past_key_values
-          past_ids: past generated ids
-      """
-      video_path = state.get('video_path', None)
-      conversation = []
-      past_ids = state.get('past_ids', None)
-      content = [{"type": "text", "text": message}]
-      if past_ids is None and video_path: # only use once
-          content.insert(0, {"type": "video", "video": video_path})
-      conversation.append({"role": "user", "content": content})
-      image_inputs, video_inputs = process_vision_info(conversation)
-      texts = self.processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True, return_tensors='pt')
-      if past_ids is not None:
-          texts = '<|im_end|>\n' + texts[self.system_prompt_offset:]
-      inputs = self.processor(
-          text=texts,
-          images=image_inputs,
-          videos=video_inputs,
-          return_tensors="pt",
-          return_attention_mask=False
-      )
-      inputs.to(self.model.device)
-      if past_ids is not None:
-          inputs['input_ids'] = torch.cat([past_ids, inputs.input_ids], dim=1) 
-      outputs = self.model.generate(
-          **inputs, past_key_values=state.get('past_key_values', None), 
-          return_dict_in_generate=True, do_sample=do_sample, 
-          repetition_penalty=repetition_penalty,
-          max_new_tokens=512,
-      )
-      state['past_key_values'] = outputs.past_key_values
-      state['past_ids'] = outputs.sequences[:, :-1]
-      response = self.processor.decode(outputs.sequences[0, inputs.input_ids.size(1):], skip_special_tokens=True)
-      return response, state
-
-model_path = 'chenjoya/LiveCC-7B-Base'
-# download a test video at: https://github.com/showlab/livecc/blob/main/demo/sources/howto_fix_laptop_mute_1080p.mp4
-video_path = "demo/sources/howto_fix_laptop_mute_1080p.mp4"
-
-infer = LiveCCDemoInfer(model_path=model_path)
-state = {'video_path': video_path}
-# first round
-query1 = 'What is the video?'
-response1, state = infer.video_qa(message=query1, state=state)
-print(f'Q1: {query1}\nA1: {response1}')
-# second round
-query2 = 'How do you know that?'
-response2, state = infer.video_qa(message=query2, state=state)
-print(f'Q2: {query2}\nA2: {response2}')
-```
-
 ## Limitations
 
 - This model is only performed video-ASR streaming pre-training, so it may not support well in common video qa.