Instructions to use jsun39/Cosine-Beta-KD-Instance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsun39/Cosine-Beta-KD-Instance with Transformers:

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

pipe = pipeline("image-text-to-text", model="jsun39/Cosine-Beta-KD-Instance")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jsun39/Cosine-Beta-KD-Instance", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jsun39/Cosine-Beta-KD-Instance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsun39/Cosine-Beta-KD-Instance"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsun39/Cosine-Beta-KD-Instance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jsun39/Cosine-Beta-KD-Instance

SGLang

How to use jsun39/Cosine-Beta-KD-Instance 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 "jsun39/Cosine-Beta-KD-Instance" \
    --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": "jsun39/Cosine-Beta-KD-Instance",
		"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 "jsun39/Cosine-Beta-KD-Instance" \
        --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": "jsun39/Cosine-Beta-KD-Instance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jsun39/Cosine-Beta-KD-Instance with Docker Model Runner:
```
docker model run hf.co/jsun39/Cosine-Beta-KD-Instance
```

jsun39 commited on 10 days ago

Commit

2bde80f

verified ·

1 Parent(s): 713e5ed

Add model card

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -82,8 +82,8 @@ DeepSpeed optimizer / RNG / trainer states are intentionally **not** uploaded.
 @article{sun2026betakd,
   title   = {Beta-KD: Uncertainty-Aware Knowledge Distillation for Multimodal
              Large Language Models},
-  author  = {Sun, Jingchen and others},
-  journal = {arXiv preprint arXiv:2603.21426},
   year    = {2026}
 }
 ```

 @article{sun2026betakd,
   title   = {Beta-KD: Uncertainty-Aware Knowledge Distillation for Multimodal
              Large Language Models},
+  author  = {Sun, Jingchen and Han, Shaobo and Patel, Deep and Kohno, Wataru and Jin, Can and Chen, Changyou},
+  journal = {CVPR},
   year    = {2026}
 }
 ```