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/README.md from Jack04810/RAVEC: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/Jack04810/RAVEC/resolve/main/RAVEC-7B/README.md
- Command line
-
hf download hf://Jack04810/RAVEC/RAVEC-7B/README.md
-
curl -L -o README.md https://huggingface.co/Jack04810/RAVEC/resolve/main/RAVEC-7B/README.md
RAVEC-7B: main-result checkpoint
This is the M1 step100 Actor export used for RAVEC Table 2 (Avg 61.00), derived from Qwen/Qwen2.5-VL-7B-Instruct.
All 15 original model files match the SHA-256 hashes recorded in all 14 evaluation runs. No conversion or quantization was applied. See checkpoint_manifest.json for the exact source, file hashes and ten benchmark scores; evaluation_summary.json retains the original evaluation summary. The reported ten-benchmark average differs from the descriptive average across all 14 evaluation tasks.
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
repo = "Jack04810/RAVEC"
processor = AutoProcessor.from_pretrained(repo, subfolder="RAVEC-7B")
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
repo, subfolder="RAVEC-7B", torch_dtype="auto", device_map="auto"
)
The original export records Transformers 4.51.3. Original configuration and tensor files are preserved unchanged.
License
The 7B checkpoint derives from Qwen2.5-VL-7B-Instruct under Apache-2.0. The base model card declares Apache-2.0; the accompanying LICENSE contains the official Apache-2.0 text. This is a fine-tuned derivative released as RAVEC; its weights differ from the base model.