Update README.md

README.md

@@ -115,7 +115,7 @@ Limitations: Although we have made efforts to ensure the safety of the model dur
 
 ## Quick Start
 
-We provide an example code to run InternVL2-1B using `transformers`.
+We provide an example code to run `InternVL2-1B` using `transformers`.
 
 > Please use transformers>=4.37.2 to ensure the model works normally.
 
@@ -150,21 +150,6 @@ model = AutoModel.from_pretrained(
     trust_remote_code=True).eval()
 ```
 
-#### BNB 4-bit Quantization
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModel
-path = "OpenGVLab/InternVL2-1B"
-model = AutoModel.from_pretrained(
-    path,
-    torch_dtype=torch.bfloat16,
-    load_in_4bit=True,
-    low_cpu_mem_usage=True,
-    use_flash_attn=True,
-    trust_remote_code=True).eval()
-```
-
 #### Multiple GPUs
 
 The reason for writing the code this way is to avoid errors that occur during multi-GPU inference due to tensors not being on the same device. By ensuring that the first and last layers of the large language model (LLM) are on the same device, we prevent such errors.
@@ -423,7 +408,7 @@ response, history = model.chat(tokenizer, pixel_values, question, generation_con
                                num_patches_list=num_patches_list, history=None, return_history=True)
 print(f'User: {question}\nAssistant: {response}')
 
-question = 'Describe this video in detail. Don\'t repeat.'
+question = 'Describe this video in detail.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list, history=history, return_history=True)
 print(f'User: {question}\nAssistant: {response}')