Image Segmentation
Transformers
Safetensors
English
calico
text-generation
computer-vision
semantic-segmentation
co-segmentation
part-segmentation
multi-image-reasoning
vision-language
Instructions to use PLAN-Lab/CALICO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PLAN-Lab/CALICO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="PLAN-Lab/CALICO")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("PLAN-Lab/CALICO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +87 -0
- added_tokens.json +9 -0
- config.json +72 -0
- generation_config.json +9 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +0 -0
- special_tokens_map.json +24 -0
- tokenizer.model +3 -0
- tokenizer_config.json +99 -0
README.md
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---
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language:
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- en
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library_name: transformers
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tags:
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- computer-vision
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- image-segmentation
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- semantic-segmentation
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- co-segmentation
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- part-segmentation
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- multi-image-reasoning
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- vision-language
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pipeline_tag: image-segmentation
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datasets:
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- PLAN-Lab/MixedParts
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---
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# CALICO
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CALICO is a large vision-language model for part-focused semantic co-segmentation. Given a pair of images and a natural-language prompt, CALICO identifies common objects, common parts, or unique parts and predicts segmentation masks for the referenced regions.
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This repository contains the released CALICO checkpoint. It is intended to be loaded with the [CALICO codebase](https://github.com/PLAN-Lab/CALICO).
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## Model Details
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- **Model name:** CALICO
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- **Dataset:** Mixed Parts
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- **Task:** part-focused semantic co-segmentation and segmentation-grounded vision-language reasoning
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- **Checkpoint format:** merged Hugging Face checkpoint with safetensors shards
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- **Vision-language image encoder:** Q-Former over EVA-CLIP-G visual features
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- **Grounding decoder:** SAM ViT-H mask decoder
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- **Correspondence features:** frozen DINOv2 features used by CALICO correspondence modules
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CALICO includes two correspondence modules in the language-model forward pass:
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- **Correspondence Extraction Module (CEM):** fuses Q-Former visual patch embeddings with frozen DINOv2 correspondence features.
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- **Correspondence Adaptation Module (CAM):** adapts Q-Former queries from the current language state and reintegrates correspondence-aware visual features into image-token hidden states.
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## Intended Use
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Use CALICO for research on multi-image part-focused segmentation, semantic co-segmentation, and vision-language grounding. The model is released for evaluation and fine-tuning with the CALICO repository.
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## Quick Start
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Install the CALICO environment and prepare Mixed Parts following the repository docs:
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- [Installation](https://github.com/PLAN-Lab/CALICO/blob/main/docs/INSTALL.md)
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- [Data preparation](https://github.com/PLAN-Lab/CALICO/blob/main/docs/DATA.md)
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- [Mixed Parts dataset](https://huggingface.co/datasets/PLAN-Lab/MixedParts)
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Run evaluation from the CALICO repository root:
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```bash
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python evaluate.py \
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--merged_ckpt_path PLAN-Lab/CALICO \
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--dataset_dir ./data \
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--output_save_path ./evaluate_results/calico_mixed_parts \
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--val_dataset "MixedPartsObjectVal|MixedPartsPartVal" \
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--multi_image_filepath_prefix ./data/mixed_parts_data/mixed_parts_test.json \
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--mode test \
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--compute_metrics
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```
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`--dataset_dir ./data` should contain:
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| 65 |
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```text
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data/
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├── coco_2017/
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└── mixed_parts_data/
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| 70 |
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```
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## Training and Fine-Tuning
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Fine-tuning is supported through `train.py` in the CALICO repository. See [docs/TRAINING.md](https://github.com/PLAN-Lab/CALICO/blob/main/docs/TRAINING.md) for recommended arguments and data layout.
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| 75 |
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## Citation
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If you use CALICO or Mixed Parts, please cite:
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| 79 |
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| 80 |
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```bibtex
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| 81 |
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@article{nguyen2025calico,
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| 82 |
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title={CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models},
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| 83 |
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author={Nguyen, Kiet A. and Juvekar, Adheesh and Yu, Tianjiao and Wahed, Muntasir and Lourentzou, Ismini},
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| 84 |
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journal={In Proceedings for the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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| 85 |
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year={2025}
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}
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```
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added_tokens.json
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{
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| 2 |
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"</p>": 32006,
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| 3 |
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"<bbox>": 32002,
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| 4 |
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"<im_end>": 32001,
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| 5 |
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"<im_start>": 32000,
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| 6 |
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"<p>": 32005,
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| 7 |
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"<point>": 32003,
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| 8 |
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"[SEG]": 32004
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| 9 |
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}
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config.json
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{
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| 2 |
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"_name_or_path": "PLAN-Lab/CALICO",
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| 3 |
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"architectures": [
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| 4 |
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"CALICOForCausalLM"
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| 5 |
+
],
|
| 6 |
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"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"auto_initialize_adaptors": true,
|
| 9 |
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"auto_initialize_qformer": true,
|
| 10 |
+
"bbox_token_idx": 32002,
|
| 11 |
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"bos_token_id": 1,
|
| 12 |
+
"dino_hidden_size": 768,
|
| 13 |
+
"dino_model_name": "dinov2_vitb14_reg",
|
| 14 |
+
"eos_token_id": 2,
|
| 15 |
+
"global_image_size": 224,
|
| 16 |
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"grounding_encoder": "sam_vit_h",
|
| 17 |
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"grounding_image_size": 1024,
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| 18 |
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"hidden_act": "silu",
|
| 19 |
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"hidden_size": 4096,
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| 20 |
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"image_aspect": "square",
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| 21 |
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"image_aspect_ratio": "square",
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| 22 |
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"initializer_range": 0.02,
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| 23 |
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"intermediate_size": 11008,
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| 24 |
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"layer_type": "linear",
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| 25 |
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"max_position_embeddings": 4096,
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| 26 |
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"mlp_bias": false,
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| 27 |
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"mm_projector_type": "linear",
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| 28 |
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"model_type": "calico",
|
| 29 |
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"num_attention_heads": 32,
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| 30 |
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"num_attn_heads": 8,
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| 31 |
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"num_hidden_layers": 32,
|
| 32 |
+
"num_key_value_heads": 32,
|
| 33 |
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"num_level_reg_features": 4,
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| 34 |
+
"num_query_tokens": 32,
|
| 35 |
+
"out_dim": 256,
|
| 36 |
+
"pad_token_id": 0,
|
| 37 |
+
"pretraining_tp": 1,
|
| 38 |
+
"q_former_model": "",
|
| 39 |
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"qformer_hidden_size": 768,
|
| 40 |
+
"qformer_vision_encoder": "eva_clip_g",
|
| 41 |
+
"qformer_vision_width": 1408,
|
| 42 |
+
"rms_norm_eps": 1e-05,
|
| 43 |
+
"rope_scaling": null,
|
| 44 |
+
"rope_theta": 10000.0,
|
| 45 |
+
"seg_image_tokens": [
|
| 46 |
+
[
|
| 47 |
+
32004,
|
| 48 |
+
313,
|
| 49 |
+
2382,
|
| 50 |
+
29896
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| 51 |
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],
|
| 52 |
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[
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| 53 |
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32004,
|
| 54 |
+
313,
|
| 55 |
+
2382,
|
| 56 |
+
29906
|
| 57 |
+
]
|
| 58 |
+
],
|
| 59 |
+
"seg_token_idx": 32004,
|
| 60 |
+
"tie_word_embeddings": false,
|
| 61 |
+
"torch_dtype": "bfloat16",
|
| 62 |
+
"train_mask_decoder": true,
|
| 63 |
+
"transformers_version": "4.42.3",
|
| 64 |
+
"update_layers": [
|
| 65 |
+
11,
|
| 66 |
+
22
|
| 67 |
+
],
|
| 68 |
+
"use_cache": false,
|
| 69 |
+
"use_mm_proj": true,
|
| 70 |
+
"vocab_size": 32007,
|
| 71 |
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"with_region": true
|
| 72 |
+
}
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generation_config.json
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{
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"_from_model_config": true,
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| 3 |
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"bos_token_id": 1,
|
| 4 |
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"eos_token_id": 2,
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| 5 |
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"max_length": 4096,
|
| 6 |
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"pad_token_id": 0,
|
| 7 |
+
"transformers_version": "4.42.3",
|
| 8 |
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"use_cache": false
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| 9 |
+
}
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a417a84fac860be7e6c606a3b1b66462dc481199df410655162415343aee6585
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size 4939091944
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model-00002-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00003-of-00004.safetensors
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size 4991144144
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model-00004-of-00004.safetensors
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size 2147217712
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model.safetensors.index.json
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The diff for this file is too large to render.
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<unk>",
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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size 499723
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tokenizer_config.json
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": true,
|
| 5 |
+
"added_tokens_decoder": {
|
| 6 |
+
"0": {
|
| 7 |
+
"content": "<unk>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false,
|
| 12 |
+
"special": true
|
| 13 |
+
},
|
| 14 |
+
"1": {
|
| 15 |
+
"content": "<s>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": false,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false,
|
| 20 |
+
"special": true
|
| 21 |
+
},
|
| 22 |
+
"2": {
|
| 23 |
+
"content": "</s>",
|
| 24 |
+
"lstrip": false,
|
| 25 |
+
"normalized": false,
|
| 26 |
+
"rstrip": false,
|
| 27 |
+
"single_word": false,
|
| 28 |
+
"special": true
|
| 29 |
+
},
|
| 30 |
+
"32000": {
|
| 31 |
+
"content": "<im_start>",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": true,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false,
|
| 36 |
+
"special": false
|
| 37 |
+
},
|
| 38 |
+
"32001": {
|
| 39 |
+
"content": "<im_end>",
|
| 40 |
+
"lstrip": false,
|
| 41 |
+
"normalized": true,
|
| 42 |
+
"rstrip": false,
|
| 43 |
+
"single_word": false,
|
| 44 |
+
"special": false
|
| 45 |
+
},
|
| 46 |
+
"32002": {
|
| 47 |
+
"content": "<bbox>",
|
| 48 |
+
"lstrip": false,
|
| 49 |
+
"normalized": true,
|
| 50 |
+
"rstrip": false,
|
| 51 |
+
"single_word": false,
|
| 52 |
+
"special": false
|
| 53 |
+
},
|
| 54 |
+
"32003": {
|
| 55 |
+
"content": "<point>",
|
| 56 |
+
"lstrip": false,
|
| 57 |
+
"normalized": true,
|
| 58 |
+
"rstrip": false,
|
| 59 |
+
"single_word": false,
|
| 60 |
+
"special": false
|
| 61 |
+
},
|
| 62 |
+
"32004": {
|
| 63 |
+
"content": "[SEG]",
|
| 64 |
+
"lstrip": false,
|
| 65 |
+
"normalized": true,
|
| 66 |
+
"rstrip": false,
|
| 67 |
+
"single_word": false,
|
| 68 |
+
"special": false
|
| 69 |
+
},
|
| 70 |
+
"32005": {
|
| 71 |
+
"content": "<p>",
|
| 72 |
+
"lstrip": false,
|
| 73 |
+
"normalized": true,
|
| 74 |
+
"rstrip": false,
|
| 75 |
+
"single_word": false,
|
| 76 |
+
"special": false
|
| 77 |
+
},
|
| 78 |
+
"32006": {
|
| 79 |
+
"content": "</p>",
|
| 80 |
+
"lstrip": false,
|
| 81 |
+
"normalized": true,
|
| 82 |
+
"rstrip": false,
|
| 83 |
+
"single_word": false,
|
| 84 |
+
"special": false
|
| 85 |
+
}
|
| 86 |
+
},
|
| 87 |
+
"bos_token": "<s>",
|
| 88 |
+
"clean_up_tokenization_spaces": false,
|
| 89 |
+
"eos_token": "</s>",
|
| 90 |
+
"legacy": false,
|
| 91 |
+
"model_max_length": 2300,
|
| 92 |
+
"pad_token": "<unk>",
|
| 93 |
+
"padding_side": "right",
|
| 94 |
+
"sp_model_kwargs": {},
|
| 95 |
+
"spaces_between_special_tokens": false,
|
| 96 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 97 |
+
"unk_token": "<unk>",
|
| 98 |
+
"use_default_system_prompt": false
|
| 99 |
+
}
|