Image-Text-to-Text
Transformers
PyTorch
English
mobilevlm
text-generation
multimodal
mllm
knowledge-distillation
mobilellama
Instructions to use jsun39/Cosine-Beta-KD-Task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsun39/Cosine-Beta-KD-Task 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-Task")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jsun39/Cosine-Beta-KD-Task", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jsun39/Cosine-Beta-KD-Task 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-Task" # 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-Task", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jsun39/Cosine-Beta-KD-Task
- SGLang
How to use jsun39/Cosine-Beta-KD-Task 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-Task" \ --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-Task", "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-Task" \ --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-Task", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jsun39/Cosine-Beta-KD-Task with Docker Model Runner:
docker model run hf.co/jsun39/Cosine-Beta-KD-Task
- Xet hash:
- 40f4e41556f63ce1f7084166bc1ac36eb3729d49bb9ccdd9771baf4624d9188c
- Size of remote file:
- 6.77 GB
- SHA256:
- 864d0d7ec481d3370187703d5530f08eb6e9401da2933f11c074d5e667ac6bdf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.