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SoMarkAI
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Model Details
Model Provider
SoMarkAI
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Property | Details |
|---|---|
| Developer | SoMark |
| Model family | Qwen3-VL, 4B |
| Task | Multimodal probabilistic decision making |
| Inputs | Images or video with text questions and candidate choices |
| Decision interface | Candidate-label logits, normalized candidate probabilities and an argmax decision |
| Post-training | GRPO with target-probability-weighted correctness rewards |
| Weight format | Full model, BF16 Safetensors |
| License | Apache 2.0 |
On a 195-question evaluation subset of mJev-Compositional-VQA, GRPO fine-tuning improves accuracy from 77.95% to 80.00% (+2.05 percentage points), answering four additional questions correctly.
| Model | Correct / total | Accuracy |
|---|---|---|
| Qwen3-VL-4B-Instruct — before GRPO | 152 / 195 | 77.95% |
| mJev-Qwen3-VL-4B-RLCD — after GRPO | 156 / 195 | 80.00% |
Both models were evaluated on the same questions, held out from RL training. Accuracy is the number of correct answers divided by the total number of questions. These results describe this evaluation subset; broader benchmark performance has not been established by this comparison.
Use the mJev runtime to obtain candidate probabilities and decisions. The HF path scores candidate labels with a model forward pass; it does not require generating a free-form answer.
Prepare Linux, Python 3.11+, a compatible NVIDIA GPU environment and system FFmpeg. Install the runtime and download this model:
bash
git clone https://github.com/SoMarkAI/mJev.gitcd mJevpython3 -m venv .venvsource .venv/bin/activatepython -m pip install torchcodec==0.11.0+cpu --index-url https://download.pytorch.org/whl/cpupython -m pip install -e '.[hf-vl]' huggingface_hub export MODEL_DIR="$HOME/models/mJev-Qwen3-VL-4B-RLCD"hf download SoMarkAI/mJev-Qwen3-VL-4B-RLCD --local-dir "$MODEL_DIR" CUDA_VISIBLE_DEVICES=0 python demo_hf.py \ --model "$MODEL_DIR" \ --input examples/motion-demo/input.json \ --mode causal --numerics stable --projection full \ --prefix-cache --question-batch-size 3 \ --output outputs/mjev-decisions.jsonThe example asks three questions about a project-created video. Inspect outputs/mjev-decisions.json for decisions and candidate scores. For your own image, create an input JSON file with this structure and pass it to --input:
json
{ "image": "your-image.png", "context": "Inspect the supplied picture.", "questions": [ { "question": "Which object is visible?", "candidates": ["A bicycle", "A car", "A bus"] } ]}Media paths are relative to the input JSON file. See the HF guide for the Python API, video inputs and memory settings. The weights retain the standard Qwen3-VL architecture and can also be loaded with Transformers; use the mJev runtime for the candidate-scoring interface described here.
Before RL, a frozen candidate scorer records the probability p_target assigned to the ground-truth option. Each sampled label receives +p_target when correct and -p_target when incorrect or invalid. Rewards are bounded in [-1, 1], and examples with higher target probabilities produce stronger training signals.
GRPO rollouts are constrained to a single candidate label, with no separate format reward. Reward scaling is disabled to preserve the target-probability weighting. A separate KL penalty limits drift from the reference model. Training updates the language model while keeping the vision tower and aligner frozen.
Released under Apache 2.0. mJev builds on Qwen3-VL-4B-Instruct; we thank the Qwen team for the base model and the Jev project for the decision-oriented interface inspiration. Evaluation data retains its own license and terms.
For issues, implementation details and contributions, visit SoMarkAI/mJev.
git clone https://github.com/SoMarkAI/mJev.gitcd mJevpython3 -m venv .venvsource .venv/bin/activatepython -m pip install torchcodec==0.11.0+cpu --index-url https://download.pytorch.org/whl/cpupython -m pip install -e '.[hf-vl]' huggingface_hub export MODEL_DIR="$HOME/models/mJev-Qwen3-VL-4B-RLCD"hf download SoMarkAI/mJev-Qwen3-VL-4B-RLCD --local-dir "$MODEL_DIR" CUDA_VISIBLE_DEVICES=0 python demo_hf.py \ --model "$MODEL_DIR" \ --input examples/motion-demo/input.json \ --mode causal --numerics stable --projection full \ --prefix-cache --question-batch-size 3 \ --output outputs/mjev-decisions.json{ "image": "your-image.png", "context": "Inspect the supplied picture.", "questions": [ { "question": "Which object is visible?", "candidates": ["A bicycle", "A car", "A bus"] } ]}