Image-Text-to-Text
PEFT
Safetensors
MLX
English
typed-decisions
jev
decision-model
vision-language
calibrated-probabilities
lora
Eval Results
Instructions to use mohit67890/imajev-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mohit67890/imajev-4b with PEFT:
Task type is invalid.
- MLX
How to use mohit67890/imajev-4b with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mohit67890/imajev-4b") config = load_config("mohit67890/imajev-4b") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Add DecisionBench evaluation result
#2
by stephen-solka - opened
Adds the existing published DecisionBench v1.0 result to the model's Evaluation results section.
Primary accuracy is 77.5481% over all 23,900 requested rows. The run has 23,363 successful rows, 537 unsupported rows counted as incorrect, and zero errors. Model revision evaluated: 712891d1192c6491441a19bdea254d8a6e1048d0.
The YAML links to the immutable published result record, which includes the evaluation setup, dataset revision, companion metrics, and raw-artifact provenance. This submission uses the existing evaluation without rerunning the model.