Update README.md

README.md

@@ -81,7 +81,7 @@ The training pipeline for a single model in InternVL 2.5 is structured across th
 
 We introduce a progressive scaling strategy to align the vision encoder with LLMs efficiently. This approach trains with smaller LLMs first (e.g., 20B) to optimize foundational visual capabilities and cross-modal alignment before transferring the vision encoder to larger LLMs (e.g., 72B) without retraining. This reuse skips intermediate stages for larger models.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/AVb_PSxhJq1z2eUFNYoqQ.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/UoNUyS7ctN5pBxNv9KnzH.png)
 
 Compared to Qwen2-VL's 1.4 trillion tokens, InternVL2.5-78B uses only 120 billion tokens—less than one-tenth. This strategy minimizes redundancy, maximizes pre-trained component reuse, and enables efficient training for complex vision-language tasks.
 
@@ -164,7 +164,7 @@ As shown in the following figure, from InternVL 1.5 to 2.0 and then to 2.5, the
 
 ### Video Understanding
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/uD5aYt2wNYL94Xn8MOVih.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/tcwH-i1qc8H16En-7AZ5M.png)
 
 ## Evaluation on Language Capability
 
@@ -510,10 +510,10 @@ Many repositories now support fine-tuning of the InternVL series models, includi
 
 ### LMDeploy
 
-LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.
+LMDeploy is a toolkit for compressing, deploying, and serving LLMs & VLMs.
 
 ```sh
-pip install lmdeploy>=0.5.3
+pip install lmdeploy>=0.6.4
 ```
 
 LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline.
@@ -537,8 +537,6 @@ If `ImportError` occurs while executing this case, please install the required d
 
 When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased.
 
-question = 'Describe this video in detail.'
-
 ```python
 from lmdeploy import pipeline, TurbomindEngineConfig
 from lmdeploy.vl import load_image
@@ -602,7 +600,7 @@ print(sess.response.text)
 LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
 
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL2_5-1B --backend turbomind --server-port 23333
+lmdeploy serve api_server OpenGVLab/InternVL2_5-1B --server-port 23333
 ```
 
 To use the OpenAI-style interface, you need to install OpenAI:

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "other"}