Instructions to use OmTheLast/jee-solver-experimental-mlx-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OmTheLast/jee-solver-experimental-mlx-adapters 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("OmTheLast/jee-solver-experimental-mlx-adapters") config = load_config("OmTheLast/jee-solver-experimental-mlx-adapters") # 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
Download release.json from OmTheLast/jee-solver-experimental-mlx-adapters: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
-
https://huggingface.co/OmTheLast/jee-solver-experimental-mlx-adapters/resolve/main/release.json
- Command line
-
hf download hf://OmTheLast/jee-solver-experimental-mlx-adapters/release.json
-
curl -L -o release.json https://huggingface.co/OmTheLast/jee-solver-experimental-mlx-adapters/resolve/main/release.json
1.71 kB
| { | |
| "status": "experimental_no_checkpoint_selected", | |
| "built_utc": "2026-09-22T13:22:27.754575+00:00", | |
| "base": { | |
| "repo": "mlx-community/Qwen3.5-9B-4bit", | |
| "revision": "8b2b98c00a6b4d291155e4890773ca8f769aee53", | |
| "license": "apache-2.0" | |
| }, | |
| "adapters": { | |
| "reliable01-step030": { | |
| "experiment": "reliable01/experiment01", | |
| "step": 30, | |
| "training_families": 30, | |
| "last_language_layers": 3, | |
| "sha256": "63ae0624e34dcfe6813b31b0359358f0a8c1d26ecbb866911bf98469e42457b5", | |
| "selected": false | |
| }, | |
| "reliable01-step060": { | |
| "experiment": "reliable01/experiment01", | |
| "step": 60, | |
| "training_families": 30, | |
| "last_language_layers": 3, | |
| "sha256": "c1fb672774b993cc8f3a0b86c678760105484a0bbd7ef2e116f982c7c272d481", | |
| "selected": false | |
| }, | |
| "reliable01-step090": { | |
| "experiment": "reliable01/experiment01", | |
| "step": 90, | |
| "training_families": 30, | |
| "last_language_layers": 3, | |
| "sha256": "adf088b169aba6ef34995982e6fe877ad635c46a3c5f36ded2bffcd4b0a7285f", | |
| "selected": false | |
| }, | |
| "coverage16-step060": { | |
| "experiment": "reliable02/coverage01", | |
| "step": 60, | |
| "training_families": 30, | |
| "last_language_layers": 16, | |
| "sha256": "2f7fc84f9f1755b3840d32150dbbcc6e4c4c88ecfdfbbb15f44496370594c786", | |
| "selected": false | |
| }, | |
| "data60-step060": { | |
| "experiment": "reliable02/data01", | |
| "step": 60, | |
| "training_families": 60, | |
| "last_language_layers": 3, | |
| "sha256": "8d4f0f54a73d15dfbef40ffafceee8b7230b1b9175c16ea23e3305f34dc39593", | |
| "selected": false | |
| } | |
| }, | |
| "evaluation": "Validation01 development only; 12 questions; Eval01/2026 not used" | |
| } | |