---
language:
- en
base_model: microsoft/phi-1_5
base_model_relation: adapter
library_name: peft
tags:
- mol
- multimodal
- visual-question-answering
- custom-code
---

# phi1_5_siglip_patch

Mixture of Layers (MoL): patch_layer routing with k=1 and `siglip`.

This release preserves the selected checkpoint weights without merging, retraining,
quantizing, or changing their tensor values. See `inference_config.json` for the
routing settings, base model, and conversation template.

## Contents and loading

This is a LoRA adapter, not a standalone language model. Load the base model listed above, the adapter weights, and `non_lora_trainables.bin` (the multimodal projector/router weights). The base model supplies the tokenizer.

Use the custom MoL model implementation from https://github.com/SStoica12/MoL.
These files are in the repository's original checkpoint format; generic
`AutoModelForCausalLM` or PEFT-only loading is insufficient for multimodal routing.
Download the repository snapshot to a local directory before using the MoL loader.
Model source version at packaging: `398a93bd9e77a67cdd786d6d1ef9451b59ac8845` on `layerwise_coeff`; the release
manifest identifies this as a working checkout, not a tested standalone Hub model.
For Phi, the runtime must support the Phi model class as well as the SigLIP router.

Associated evaluation scripts live under `scripts/v1_5/eval` in the MoL checkout:
`vstar.sh`, `mmstar.sh`, `hrbench8k.sh`, `hrbench4k.sh`, `realworldqa.sh`,
`naturalbench.sh`, and `charxiv.sh`. Select this local package explicitly rather
than relying on the scripts' historical default checkpoint arrays. Use the
adapter base model and routing settings recorded in `inference_config.json`.

## Evaluation and provenance

Original experiment directory: `bunny-phi-1.5-siglip-router-finetune-router_lr_5e-5-patch_layer`.
`evaluation_evidence.json` contains the recovered scores for this selected run.
Some evidence is archival; these are not newly reproduced scores. A paper row
may combine multiple checkpoints, so it must not be used as this model's scorecard.

`source_config.json` preserves the original configuration; `release_manifest.json`
records the source, metadata-only configuration additions and file hashes.
Training logs, optimizer states, and intermediate checkpoint directories are omitted.
SHA256 copy verification and safetensors structure/index checks were performed;
full model inference was not run as part of packaging.

## Usage and dependencies

Intended for research on visual question answering and layer routing. Vision
encoders and, for adapters, the base language model are external dependencies.
The release does not grant new rights to the upstream models; consult their
model cards and applicable terms. No new license is assigned by this packaging step.

Model card format: https://huggingface.co/docs/hub/model-cards