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
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library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- ravec
- qwen2_5_vl
- multimodal
- reinforcement-learning
license: other
license_name: qwen-research
license_link: https://huggingface.co/Jack04810/RAVEC/blob/main/RAVEC-3B/LICENSE
---
# RAVEC: main-result checkpoints
This repository releases the verified 3B and 7B checkpoints associated with Table 2 of RAVEC.
| Directory | Base model | Checkpoint | Availability |
|---|---|---|---|
| `RAVEC-3B` | Qwen2.5-VL-3B-Instruct | F0, step 175 | Uploaded |
| `RAVEC-7B` | Qwen2.5-VL-7B-Instruct | M1, step 100 Actor | Uploaded |
The 7B release is the exact M1 step100 Actor export used by all fourteen recorded evaluation runs. All 15 original files were verified against the evaluation-time SHA-256 hashes; see `RAVEC-7B/checkpoint_manifest.json`.
The current paper and table-generation metadata identify the 3B step175 export as Actor/student.
Older records use `EMA_G3R` / `EMA` labels. The released 3B files match the original
checkpoint manifest byte-for-byte; `RAVEC-3B/checkpoint_manifest.json` records their SHA-256 hashes.
## Reported results
Avg@8 is mean accuracy over eight generations per question. Avg is the equal-weight
mean of the ten listed benchmarks. Scores are percentages.
| Model | Geometry3K | MathVista | We-Math | MathVerse | LogicVista | MMMU-Pro | MathVerse-V | C-VQA-Real | MARS-Bench | POPE | Avg |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| RAVEC-3B | 33.28 | 59.78 | 61.68 | 57.43 | 41.67 | 28.38 | 54.44 | 66.54 | 53.05 | 87.98 | 54.42 |
| RAVEC-7B | 43.20 | 66.44 | 70.73 | 67.53 | 46.28 | 36.09 | 65.03 | 70.69 | 55.99 | 87.99 | 61.00 |
See `evaluation_results.json` for available score precision, checkpoint selection, and source hashes.
## Loading the 3B checkpoint
```python
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
repo = "Jack04810/RAVEC"
processor = AutoProcessor.from_pretrained(repo, subfolder="RAVEC-3B")
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
repo, subfolder="RAVEC-3B", torch_dtype="auto", device_map="auto"
)
```
The export records Transformers 4.51.3. Use a Transformers version supporting Qwen2.5-VL.
## Loading the 7B checkpoint
Use the same loading example with `subfolder="RAVEC-7B"` for both the processor and model. See [RAVEC-7B/README.md](RAVEC-7B/README.md) for details and the original evaluation summary.
## License
The 3B checkpoint is derived from Qwen2.5-VL-3B-Instruct and retains the
Qwen Research License; see `RAVEC-3B/LICENSE` and `RAVEC-3B/NOTICE`.
The 7B checkpoint is derived from Qwen2.5-VL-7B-Instruct under Apache-2.0; see `RAVEC-7B/LICENSE`. The two model directories retain their respective base-model licenses.
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