Update README.md

README.md

@@ -30,159 +30,14 @@ Official PyTorch implementation of the model described in
 | Model Hub   | [Hugging Face](https://huggingface.co/chaoyinshe/EchoVLM) |
 
 ## 🔄 Updates
+- **Coming soon**: V2 with Chain-of-Thought reasoning and reinforcement learning enhancements—full training & inference code plus benchmark test-set will be fully open-sourced.
+- **Dec 1, 2025**: To better promote development in this field, we've open-sourced our latest instruction fine-tuned model based on Lingshu-7B. Essentially built on Qwen2.5VL, it enjoys a better ecosystem—for example, it can seamlessly leverage vLLM for accelerated inference. Released model weights on [Hugging Face](https://huggingface.co/chaoyinshe/EchoVLM_V2_lingshu_base_7b_instruct_preview).  
+- **Sep 21, 2025**: The full, uncleaned model codebase is now open-sourced on GitHub!
 - **Sep 19, 2025**: Released model weights on [Hugging Face](https://huggingface.co/chaoyinshe/EchoVLM).  
-- **Sep 17, 2025**: Paper published on [arXiv](https://arxiv.org/abs/2509.14977).  
-- **Coming soon**: V2 with Chain-of-Thought reasoning and reinforcement learning enhancements.
+- **Sep 17, 2025**: Paper published on [arXiv](https://arxiv.org/abs/2509.14977).
 
 ## 🚀 Quick Start
-### Using 🤗  Transformers to Chat
-
-Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
-
-```python
-from transformers import Qwen2VLMOEForConditionalGeneration, AutoProcessor
-from qwen_vl_utils import process_vision_info
-import torch
-
-# ===== 1. Load model & processor =====
-model = Qwen2VLMOEForConditionalGeneration.from_pretrained(
-    "chaoyinshe/EchoVLM",
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2",   # faster & memory-efficient
-    device_map="auto",
-)
-processor = AutoProcessor.from_pretrained("chaoyinshe/EchoVLM")
-# The default range for the number of visual tokens per image in the model is 4-16384.
-# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
-# min_pixels = 256*28*28
-# max_pixels = 1280*28*28
-# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "An ultrasound image",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-
-# Inference: Generation of the output
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-<details>
-<summary>Multi image inference</summary>
-
-```python
-# Messages containing multiple images and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "ultrasound image 1"},
-            {"type": "image", "image": "ultrasound image 2"},
-            {"type": "text", "text": "帮我给出超声报告"},
-        ],
-    }
-]
-
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-
-# Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-</details>
-<details>
-<summary>Batch inference</summary>
-
-```python
-# Sample messages for batch inference
-messages1 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/image1.jpg"},
-            {"type": "image", "image": "file:///path/to/image2.jpg"},
-            {"type": "text", "text": "This patient has a hypoechoic nodule in the left breast. What is the next step in treatment?"},
-        ],
-    }
-]
-messages2 = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Who are you?"},
-]
-# Combine messages for batch processing
-messages = [messages1, messages2]
-
-# Preparation for batch inference
-texts = [
-    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
-    for msg in messages
-]
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=texts,
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to("cuda")
-
-# Batch Inference
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_texts = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_texts)
-```
-</details>
+Reference [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
 
 ## 📌 Citation