Image-Text-to-Text
Transformers
Safetensors
Chinese
qwen2_vl
text-generation
medical
conversational
text-generation-inference
Instructions to use chaoyinshe/EchoVLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chaoyinshe/EchoVLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="chaoyinshe/EchoVLM") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("chaoyinshe/EchoVLM") model = AutoModelForSeq2SeqLM.from_pretrained("chaoyinshe/EchoVLM") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use chaoyinshe/EchoVLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chaoyinshe/EchoVLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chaoyinshe/EchoVLM", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/chaoyinshe/EchoVLM
- SGLang
How to use chaoyinshe/EchoVLM with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "chaoyinshe/EchoVLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chaoyinshe/EchoVLM", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "chaoyinshe/EchoVLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chaoyinshe/EchoVLM", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use chaoyinshe/EchoVLM with Docker Model Runner:
docker model run hf.co/chaoyinshe/EchoVLM
Improve model card with Model Hub link and Updates section
#1
by nielsr HF Staff - opened
README.md
CHANGED
|
@@ -1,21 +1,19 @@
|
|
| 1 |
---
|
| 2 |
-
|
|
|
|
| 3 |
language:
|
| 4 |
- zh
|
| 5 |
- en
|
|
|
|
|
|
|
| 6 |
metrics:
|
| 7 |
- bertscore
|
| 8 |
- bleu
|
| 9 |
-
base_model:
|
| 10 |
-
- Qwen/Qwen2-VL-7B-Instruct
|
| 11 |
pipeline_tag: image-text-to-text
|
| 12 |
-
library_name: transformers
|
| 13 |
tags:
|
| 14 |
- medical
|
| 15 |
---
|
| 16 |
|
| 17 |
-
|
| 18 |
-
|
| 19 |
# EchoVLM (paper implementation)
|
| 20 |
|
| 21 |
Official PyTorch implementation of the model described in
|
|
@@ -28,6 +26,12 @@ Official PyTorch implementation of the model described in
|
|
| 28 |
| Paper | [arXiv:2509.14977](https://arxiv.org/abs/2509.14977) |
|
| 29 |
| Authors | Chaoyin She¹, Ruifang Lu² |
|
| 30 |
| Code | [GitHub repo](https://github.com/Asunatan/EchoVLM) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 31 |
|
| 32 |
## 🚀 Quick Start
|
| 33 |
### Using 🤗 Transformers to Chat
|
|
@@ -192,4 +196,5 @@ If you use this model or code in your research, please cite:
|
|
| 192 |
archivePrefix={arXiv},
|
| 193 |
primaryClass={cs.CV},
|
| 194 |
url={https://arxiv.org/abs/2509.14977},
|
| 195 |
-
}
|
|
|
|
|
|
| 1 |
---
|
| 2 |
+
base_model:
|
| 3 |
+
- Qwen/Qwen2-VL-7B-Instruct
|
| 4 |
language:
|
| 5 |
- zh
|
| 6 |
- en
|
| 7 |
+
library_name: transformers
|
| 8 |
+
license: apache-2.0
|
| 9 |
metrics:
|
| 10 |
- bertscore
|
| 11 |
- bleu
|
|
|
|
|
|
|
| 12 |
pipeline_tag: image-text-to-text
|
|
|
|
| 13 |
tags:
|
| 14 |
- medical
|
| 15 |
---
|
| 16 |
|
|
|
|
|
|
|
| 17 |
# EchoVLM (paper implementation)
|
| 18 |
|
| 19 |
Official PyTorch implementation of the model described in
|
|
|
|
| 26 |
| Paper | [arXiv:2509.14977](https://arxiv.org/abs/2509.14977) |
|
| 27 |
| Authors | Chaoyin She¹, Ruifang Lu² |
|
| 28 |
| Code | [GitHub repo](https://github.com/Asunatan/EchoVLM) |
|
| 29 |
+
| Model Hub | [Hugging Face](https://huggingface.co/chaoyinshe/EchoVLM) |
|
| 30 |
+
|
| 31 |
+
## 🔄 Updates
|
| 32 |
+
- **Sep 19, 2025**: Released model weights on [Hugging Face](https://huggingface.co/chaoyinshe/EchoVLM).
|
| 33 |
+
- **Sep 17, 2025**: Paper published on [arXiv](https://arxiv.org/abs/2509.14977).
|
| 34 |
+
- **Coming soon**: V2 with Chain-of-Thought reasoning and reinforcement learning enhancements.
|
| 35 |
|
| 36 |
## 🚀 Quick Start
|
| 37 |
### Using 🤗 Transformers to Chat
|
|
|
|
| 196 |
archivePrefix={arXiv},
|
| 197 |
primaryClass={cs.CV},
|
| 198 |
url={https://arxiv.org/abs/2509.14977},
|
| 199 |
+
}
|
| 200 |
+
```
|