Instructions to use Jack04810/RAVEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jack04810/RAVEC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Jack04810/RAVEC")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jack04810/RAVEC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jack04810/RAVEC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jack04810/RAVEC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jack04810/RAVEC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jack04810/RAVEC
- SGLang
How to use Jack04810/RAVEC 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 "Jack04810/RAVEC" \ --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": "Jack04810/RAVEC", "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 "Jack04810/RAVEC" \ --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": "Jack04810/RAVEC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jack04810/RAVEC with Docker Model Runner:
docker model run hf.co/Jack04810/RAVEC
Download RAVEC-7B/evaluation_summary.json from Jack04810/RAVEC: direct link, hf CLI and curl.
- Browser
- Download file 12.8 kB
-
https://huggingface.co/Jack04810/RAVEC/resolve/main/RAVEC-7B/evaluation_summary.json
- Command line
-
hf download hf://Jack04810/RAVEC/RAVEC-7B/evaluation_summary.json
-
curl -L -o evaluation_summary.json https://huggingface.co/Jack04810/RAVEC/resolve/main/RAVEC-7B/evaluation_summary.json
12.8 kB
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| "schema": "papo.eval_summary.v2", | |
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| "protocol": { | |
| "name": "papo", | |
| "rollouts": 8, | |
| "temperature": 1.0, | |
| "max_samples": null, | |
| "max_new_tokens": 2048, | |
| "cutoff_len": 9216, | |
| "batch_size": 64, | |
| "image_max_pixels": 589824, | |
| "image_min_pixels": 3136, | |
| "top_p": 1.0, | |
| "top_k": 50, | |
| "repetition_penalty": 1.0, | |
| "seed": null, | |
| "template": "qwen2_vl", | |
| "smoke": false, | |
| "vllm_config": { | |
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| "gpu_memory_utilization": 0.7, | |
| "max_num_seqs": 64 | |
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