Update README.md

README.md

@@ -38,25 +38,10 @@ tags:
 
 This repository hosts the downstream MLLM checkpoints accompanying **[A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models](https://arxiv.org/abs/2610.05413)**.
 
-## Files
-
-Checkpoints are grouped by language model backbone, vision encoder, and training stage:
-
-```text
-models/
-  continuous/<llm>/<encoder>/
-    pretrain/mm_projector.bin
-    pretrain/config.json
-    finetune/                 # Model weights, config, and tokenizer files
-```
-
-Use `qwen25`, `qwen3`, or `smollm2` for the three main language-model groups.
-For example, the Qwen3 + CLIP-L/14 finetuned model is at `models/continuous/qwen3/clip_openai__l14/finetune/`. 
-
 # Model Zoo
 
-This Model Zoo covers the **70 visual tokenizers used in our paper**: **43 language-supervised**, **22 self-supervised**, and **5 discrete** tokenizers. 
-The current release provides both pretraining and finetuning checkpoints for all **65 continuous tokenizers** with each of the three main language backbones. The **5 discrete tokenizers** are listed separately with their checkpoint availability. 
+This Model Zoo covers the **70 visual encoders used in our paper**: **43 language-supervised**, **22 self-supervised**, and **5 discrete** tokenizers. 
+The current release provides both pretraining and finetuning checkpoints for all **65 continuous vision encoders** with each of the three main language backbones. 
 
 ## Training data
 
@@ -65,15 +50,13 @@ The current release provides both pretraining and finetuning checkpoints for all
 
 
 ## Downloads
-- 'Encoder weights' links to the original upstream vision encoder or visual tokenizer. Each encoder name links to its model or project page. Use these frozen encoder weights together with the corresponding Stage 1 projector or Stage 2 MLLM checkpoint, preserving the architecture, feature layer and preprocessing specified by that run's `config.json`.
+- `Encoder weights` links to the original upstream vision encoder or visual tokenizer. Each encoder name links to its model or project page. Use these frozen encoder weights together with the corresponding Stage 1 projector or Stage 2 MLLM checkpoint, preserving the architecture, feature layer and preprocessing specified by that run's `config.json`.
   - For Hugging Face models, retain the associated configuration and processor files. **Web-SSL MAE 3B** has sharded weights; its link opens the complete model repository.
   - **DINOv3** requires acceptance of the upstream model's terms and authentication. The **RAEv2 DINOv3-L (k=7)** entry uses the same DINOv3-L/16 backbone with the project's k=7 intermediate-layer readout; its download is the backbone used to construct that representation.
-  - **TokLIP-S/L** require both the encoder checkpoint and the linked **VQ** checkpoint. For **UniAR** and **VILA-U**, retain the config files alongside the weights in the respective `bsq_encoder/` and `vision_tower/` directories.
 - `projector` downloads the pretraining `mm_projector.bin`;
 - `finetuned` opens the finetuned checkpoint directory, including model weights, configuration, and language-tokenizer files. The frozen vision encoder must be supplied separately using the matching architecture and weights recorded in `config.json`. Pretraining projector weights alone are not instruction-tuned MLLMs.
 
-### Qwen2.5-1.5B-Instruct
-Base LLM: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
+### [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
 
 | Base LLM | Vision Encoder / Tokenizer | Encoder weights | Stage 1 Pretrained weights | Stage 2 Finetuned weights |
 | --- | --- | --- | --- | --- |
@@ -144,9 +127,7 @@ Base LLM: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-
 | Qwen2.5-1.5B-Instruct | [EUPE ViT-T](https://huggingface.co/facebook/EUPE-ViT-T) | [encoder](https://huggingface.co/facebook/EUPE-ViT-T/resolve/main/EUPE-ViT-T.pt?download=true) | [projector](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo/resolve/main/continuous/qwen25/eupe_vit_t/pretrain/mm_projector.bin) | [finetuned](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo/tree/main/continuous/qwen25/eupe_vit_t/finetune) |
 
 
-### Qwen3-1.7B
-
-Base LLM: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
+### [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
 
 | Base LLM | Vision Encoder / Tokenizer | Encoder weights | Stage 1 Pretrained weights | Stage 2 Finetuned weights |
 | --- | --- | --- | --- | --- |
@@ -217,9 +198,7 @@ Base LLM: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
 | Qwen3-1.7B | [EUPE ViT-T](https://huggingface.co/facebook/EUPE-ViT-T) | [encoder](https://huggingface.co/facebook/EUPE-ViT-T/resolve/main/EUPE-ViT-T.pt?download=true) | [projector](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo/resolve/main/continuous/qwen3/eupe_vit_t/pretrain/mm_projector.bin) | [finetuned](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo/tree/main/continuous/qwen3/eupe_vit_t/finetune) |
 
 
-### SmolLM2-1.7B-Instruct
-
-Base LLM: [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct).
+### [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
 
 | Base LLM | Vision Encoder / Tokenizer | Encoder weights | Stage 1 Pretrained weights | Stage 2 Finetuned weights |
 | --- | --- | --- | --- | --- |