Instructions to use echo840/Monkey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use echo840/Monkey with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="echo840/Monkey", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("echo840/Monkey", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use echo840/Monkey with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "echo840/Monkey" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "echo840/Monkey", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/echo840/Monkey
- SGLang
How to use echo840/Monkey 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 "echo840/Monkey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "echo840/Monkey", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "echo840/Monkey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "echo840/Monkey", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use echo840/Monkey with Docker Model Runner:
docker model run hf.co/echo840/Monkey
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,7 +7,7 @@ Zhang Li*, Biao Yang*, Qiang Liu, Zhiyin Ma, Shuo Zhang, Jingxu Yang, Yabo Sun,
|
|
| 7 |
<strong>Huazhong University of Science and Technology, Kingsoft</strong>
|
| 8 |
</div>
|
| 9 |
<p align="center">
|
| 10 |
-
<a href="https://arxiv.org/abs/2311.06607">Paper</a>   |   <a href="http://
|
| 11 |
<!-- |   <a href="Monkey Model">Monkey Models</a>  |   <a href="http://huggingface.co/echo840/Monkey">Tutorial</a> -->
|
| 12 |
</p>
|
| 13 |
|
|
@@ -36,9 +36,6 @@ pip install -r requirements.txt
|
|
| 36 |
|
| 37 |
## Demo
|
| 38 |
|
| 39 |
-
[Demo](http://27.17.252.152:7680/) is fast and easy to use. Simply uploading an image from your desktop or phone, or capture one directly.
|
| 40 |
-
[Demo_chat](http://27.17.252.152:7681/) is also launched as an upgraded version of the original demo to deliver an enhanced interactive experience.
|
| 41 |
-
|
| 42 |
Before 14/11/2023, we have observed that for some random pictures Monkey can achieve more accurate results than GPT4V.
|
| 43 |
|
| 44 |
|
|
|
|
| 7 |
<strong>Huazhong University of Science and Technology, Kingsoft</strong>
|
| 8 |
</div>
|
| 9 |
<p align="center">
|
| 10 |
+
<a href="https://arxiv.org/abs/2311.06607">Paper</a>   |   <a href="http://huggingface.co/datasets/echo840/Detailed_Caption">Detailed Caption</a>   |   <a href="http://huggingface.co/echo840/Monkey">Model Weight</a>   | <a href="https://www.wisemodel.cn/models/HUST-VLRLab/Monkey/">Model Weight in wisemodel</a>  
|
| 11 |
<!-- |   <a href="Monkey Model">Monkey Models</a>  |   <a href="http://huggingface.co/echo840/Monkey">Tutorial</a> -->
|
| 12 |
</p>
|
| 13 |
|
|
|
|
| 36 |
|
| 37 |
## Demo
|
| 38 |
|
|
|
|
|
|
|
|
|
|
| 39 |
Before 14/11/2023, we have observed that for some random pictures Monkey can achieve more accurate results than GPT4V.
|
| 40 |
|
| 41 |
|