Instructions to use LINs-lab/DynMoE-Qwen-1.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LINs-lab/DynMoE-Qwen-1.8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LINs-lab/DynMoE-Qwen-1.8B", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LINs-lab/DynMoE-Qwen-1.8B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LINs-lab/DynMoE-Qwen-1.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LINs-lab/DynMoE-Qwen-1.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LINs-lab/DynMoE-Qwen-1.8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LINs-lab/DynMoE-Qwen-1.8B

SGLang

How to use LINs-lab/DynMoE-Qwen-1.8B 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 "LINs-lab/DynMoE-Qwen-1.8B" \
    --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": "LINs-lab/DynMoE-Qwen-1.8B",
		"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 "LINs-lab/DynMoE-Qwen-1.8B" \
        --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": "LINs-lab/DynMoE-Qwen-1.8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LINs-lab/DynMoE-Qwen-1.8B with Docker Model Runner:
```
docker model run hf.co/LINs-lab/DynMoE-Qwen-1.8B
```

Improve model card: Correct pipeline tag, add library name, update news section

by nielsr HF Staff - opened Mar 11, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

Files changed (1) hide show

README.md +9 -4

README.md CHANGED Viewed

@@ -1,6 +1,7 @@
 ---
 license: apache-2.0
-pipeline_tag: image-text-to-text
 ---
 <h2 align="center"> <a href="https://arxiv.org/abs/2405.14297">Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models</a></h2>
@@ -9,8 +10,8 @@ pipeline_tag: image-text-to-text
 ## 📰 News
-- **[2024.05.31]** 🔥 Our [code](https://github.com/LINs-lab/DynMoE/) is released!
-- **[2024.05.25]** 🔥 Our **checkpoints** are available now!
 - **[2024.05.23]** 🔥 Our [paper](https://arxiv.org/abs/2405.14297) is released!
 ## 😎 What's Interesting?
@@ -57,4 +58,8 @@ This project is released under the Apache-2.0 license as found in the [LICENSE](
       archivePrefix={arXiv},
       primaryClass={cs.LG}
 }
-```

 ---
 license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 <h2 align="center"> <a href="https://arxiv.org/abs/2405.14297">Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models</a></h2>
 ## 📰 News
+- **[2025.01.23]**: 🎉 Our paper is accepted to ICLR 2025!
+- **[2024.05.25]** 🔥 Our [checkpoints](https://huggingface.co/collections/LINs-lab/dynmoe-family-665ed5a331a7e84463cab01a) are available now!
 - **[2024.05.23]** 🔥 Our [paper](https://arxiv.org/abs/2405.14297) is released!
 ## 😎 What's Interesting?
       archivePrefix={arXiv},
       primaryClass={cs.LG}
 }
+```
+## Star History
+[![Star History Chart](https://api.star-history.com/svg?repos=LINs-lab/DynMoE&type=Date)](https://star-history.com/#LINs-lab/DynMoE&Date)