Update README.md

README.md

@@ -25,8 +25,21 @@ pipeline_tag: visual-question-answering
 **InternLM-XComposer2.5** excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend. IXC2.5 is trained with 24K interleaved image-text contexts, it can seamlessly extend to 96K long contexts via RoPE extrapolation. This long-context capability allows IXC-2.5 to excel in tasks requiring extensive input and output contexts. 
 
 
+## 4-Bit Model
+We offer 4-bit quantized models via LMDeploy to reduce memory requirements. For a memory usage comparison, please refer to [here](example_code/4bit/README.md).
+
+```python
+from lmdeploy import TurbomindEngineConfig, pipeline
+from lmdeploy.vl import load_image
+engine_config = TurbomindEngineConfig(model_format='awq')
+pipe = pipeline('internlm/internlm-xcomposer2d5-7b-4bit', backend_config=engine_config)
+image = load_image('examples/dubai.png')
+response = pipe(('describe this image', image))
+print(response.text)
+```
+
 ### Import from Transformers
-To load the InternLM-XComposer2-4KHD model using Transformers, use the following code:
+To load the InternLM-XComposer2.5 model using Transformers, use the following code:
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM