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 evaluation_results.json from Jack04810/RAVEC: direct link, hf CLI and curl.
- Browser
- Download file 3.89 kB
-
https://huggingface.co/Jack04810/RAVEC/resolve/main/evaluation_results.json
- Command line
-
hf download hf://Jack04810/RAVEC/evaluation_results.json
-
curl -L -o evaluation_results.json https://huggingface.co/Jack04810/RAVEC/resolve/main/evaluation_results.json
3.89 kB
| { | |
| "table": "Table 2", | |
| "metric": "Avg@8 (%)", | |
| "average": "Equal weight over ten benchmarks", | |
| "checkpoint_selection": { | |
| "F0": { | |
| "checkpoint_step": 175, | |
| "weights": "Actor" | |
| }, | |
| "M1": { | |
| "checkpoint_step": 100, | |
| "weights": "Actor", | |
| "reason": "Single checkpoint with all fourteen benchmarks; also the highest recorded CLEAN2K score." | |
| } | |
| }, | |
| "rows": { | |
| "RAVEC-3B": { | |
| "source_scores_percent": { | |
| "Geometry3K": "33.28", | |
| "MathVista": "59.78", | |
| "We-Math": "61.68", | |
| "MathVerse": "57.43", | |
| "LogicVista": "41.67", | |
| "MMMU-Pro": "28.38", | |
| "MathVerse-V": "54.44", | |
| "C-VQA-Real": "66.5414281170483500", | |
| "MARS-Bench": "53.0454990215264200", | |
| "POPE": "87.9833333333333400" | |
| }, | |
| "benchmark_count": 10, | |
| "missing_benchmarks": [], | |
| "mean_percent": "54.4230260471908110", | |
| "display_mean_percent": "54.42" | |
| }, | |
| "RAVEC-7B": { | |
| "source_scores_percent": { | |
| "Geometry3K": "43.20", | |
| "MathVista": "66.44", | |
| "We-Math": "70.73", | |
| "MathVerse": "67.53", | |
| "LogicVista": "46.28", | |
| "MMMU-Pro": "36.09", | |
| "MathVerse-V": "65.03", | |
| "C-VQA-Real": "70.69", | |
| "MARS-Bench": "55.99", | |
| "POPE": "87.99" | |
| }, | |
| "benchmark_count": 10, | |
| "missing_benchmarks": [], | |
| "mean_percent": "60.997", | |
| "display_mean_percent": "61.00", | |
| "raw_scores_percent": { | |
| "geo3k": 43.19883527454243, | |
| "mathvista": 66.4375, | |
| "wemath": 70.72557471264368, | |
| "mathverse": 67.53440366972477, | |
| "logicvista": 46.28076062639821, | |
| "mmmu-pro": 36.09104046242775, | |
| "mathverse-v": 65.03249235474006, | |
| "c-vqa-real": 70.68821564885496, | |
| "mars-bench": 55.990704500978474, | |
| "pope": 87.99166666666667 | |
| }, | |
| "raw_mean_percent": 60.997119391697694 | |
| } | |
| }, | |
| "availability": { | |
| "RAVEC-3B": "included", | |
| "RAVEC-7B": "included; M1 step100 Actor, verified against evaluation-time SHA-256 hashes" | |
| }, | |
| "provenance": [ | |
| { | |
| "source_in_project": "paper/tables/main_results.tex", | |
| "sha256": "6f7c33a0076b6e649c3bc44a4ee600731d3a8f722be5aacf23f567ded0745361" | |
| }, | |
| { | |
| "source_in_project": "paper/scripts/build_experiment_tables.py", | |
| "sha256": "fd2a5c3e3d16d9a2501351729b5d5f6099174bc921f82b15094de83a3627fc95" | |
| }, | |
| { | |
| "source_in_project": "paper/data/table_statistics_20261107.json", | |
| "sha256": "5e42aba7c5ebf532153f9a14e45665c589cc611e6a1f9151c64a3ef4a6d2f80d" | |
| }, | |
| { | |
| "source_in_project": "paper/data/ravec3b_extra_scores_20261108.json", | |
| "sha256": "0fece581e263c3bbc7fd0054cb7938af45b4088ac7234abb332e05b72bede3a8" | |
| }, | |
| { | |
| "source_in_project": "checkpoints/ravec_main/step175/manifest.json", | |
| "sha256": "e95a6a5de5794b5f10f1cbad73f4dd6ed22308dd4a397abf0deda4cce64374e2" | |
| }, | |
| { | |
| "source_in_project": "RAVEC_GRPO_7B_RESULTS_20261106.md", | |
| "sha256": "4ecf81e9dfe50c71c16fc51a0c92134807bc41234ad43cec8f471908404b6a35" | |
| }, | |
| { | |
| "source_in_project": "runtime/assets/runs/formal_7b_4gpu_20261029_000339/M1/eval_step100_suite14/238fbe483d8179bb/summary.json", | |
| "sha256": "87f95c152d4ebc6d193018d7e1648677d2f375012fd35e00c7e6144fcf797329", | |
| "model_fingerprint": "238fbe483d8179bbbab0ce9f4ec044c09bb43661f4f319698f063beb2f6199d7", | |
| "release_manifest": "RAVEC-7B/checkpoint_manifest.json" | |
| } | |
| ], | |
| "precision_note": "3B original seven displayed tasks and 7B tasks use reported precision; 3B CVQA/MARS/POPE use raw scoring precision.", | |
| "weight_label_note": "Historical 3B records use EMA_G3R / EMA labels; current paper and table-generation metadata identify this exact step175 export as Actor/student. File hashes identify the released artifact unambiguously.", | |
| "release_scope": "3B and 7B main-result checkpoints" | |
| } | |