SEELE-AI

EVA01-2B-Instruct-LoRA

README

License: apache-2.0

Model Details

Table with columns: Item, Description
Item	Description
Model	`SEELE-AI/EVA01-2B-Instruct-LoRA`
Release type	UND-side LoRA adapter
Base model	`Qwen/Qwen3-VL-2B-Instruct`
3D input	`.glb` mesh input through the EVA01 mesh UND processor
Components	PEFT adapter, EVA01 mesh UND encoder, connector, tokenizer, processor, and config files
Training recipe	Alignment followed by instruction tuning
Code	SeeleAI/OpenEVA
Project page	EVA01
Paper	arXiv:2605.16745

This repo does not include the full Qwen3-VL base weights. The EVA01 loader resolves the base model from the LoRA config by default. For offline or local deployment, pass a local base path through base_model_name_or_path.

Installation

EVA01 requires the OpenEVA code package in addition to the checkpoint files.

bash

git clone https://github.com/SeeleAI/OpenEVA.gitcd OpenEVA/EVA01bash install.shsource .venv/bin/activate

If a different CUDA wheel index is required, set TORCH_INDEX_URL before running the install script.

bash

TORCH_INDEX_URL=https://download.pytorch.org/whl/cu121 bash install.sh

Quick Inference

CLI:

bash

python infer.py \  --checkpoint SEELE-AI/EVA01-2B-Instruct-LoRA \  --mesh assets/examples/construction_backhoe.glb \  --question "Describe this 3D object in detail."

Python API:

python

import torchfrom eva01 import EVA01ForConditionalGeneration, EVA01Processor model = EVA01ForConditionalGeneration.from_pretrained(    "SEELE-AI/EVA01-2B-Instruct-LoRA",    torch_dtype=torch.bfloat16,    device_map="auto",)processor = EVA01Processor.from_pretrained("SEELE-AI/EVA01-2B-Instruct-LoRA") messages = [{    "role": "user",    "content": [        {"type": "mesh", "mesh": "assets/examples/construction_backhoe.glb"},        {"type": "text", "text": "Describe this 3D object in detail."},    ],}] inputs = processor.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_dict=True,    return_tensors="pt",).to(model.device) output_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)text = processor.batch_decode(    output_ids[:, inputs.input_ids.shape[1]:],    skip_special_tokens=True,    clean_up_tokenization_spaces=False,)[0]print(text)

Local base override:

python

model = EVA01ForConditionalGeneration.from_pretrained(    "SEELE-AI/EVA01-2B-Instruct-LoRA",    base_model_name_or_path="/path/to/Qwen3-VL-2B-Instruct",    torch_dtype=torch.bfloat16,    device_map="auto",)

The public mesh token is <|mesh_und_pad|>. The processor returns input_ids, attention_mask, and mesh_und_values.

Gradio Chat

bash

python app.py --checkpoint SEELE-AI/EVA01-2B-Instruct-LoRA --host 127.0.0.1 --port 7860

The OpenEVA app supports uploaded .glb files and includes 10 built-in TexVerse examples. The OpenEVA GitHub repository also contains a PBR-rendered example gallery.

PointLLM-200 Evaluation

The OpenEVA eval script downloads PointLLM-200 benchmark files staged in the Full checkpoint repo and writes results under EVA01/outputs/pointllm200/.

bash

python eval_pointllm200.py --variant lora

The deterministic path computes BLEU, ROUGE, METEOR, Sentence-BERT, and SimCSE with fixed seed 20260615 and greedy generation. GPT-ref and GPT-img judge paths are available when OPENAI_API_KEY is set.

The PointLLM-200 benchmark source is cited as RunsenXu/PointLLM.

Metrics

All metrics below are reported on PointLLM-200 with 200 samples, fixed seed 20260615, and greedy decoding. GPT-ref and GPT-img are judge metrics and may vary with judge model and API settings.

Table with columns: Model, B-1, B-4, R-L, METEOR, SBERT, SimCSE, GPT-ref, GPT-img
Model	B-1	B-4	R-L	METEOR	SBERT	SimCSE	GPT-ref	GPT-img
PointLLM-13B	7.873	0.649	10.519	13.620	47.539	48.602	51.735	49.745
ShapeLLM-13B	10.542	1.050	12.954	14.234	39.935	40.728	33.925	35.870
ShapeLLM-Omni	11.326	1.197	14.190	13.276	34.617	35.115	25.625	20.190
EVA01-2B-Instruct	6.386	0.589	9.443	13.505	50.651	50.767	59.045	70.335
EVA01-2B-Instruct-LoRA	6.372	0.607	9.455	13.567	51.194	51.320	59.560	71.480

Intended Use

This checkpoint is intended for research and development around 3D mesh understanding, 3D asset captioning, and mesh-grounded question answering. It expects mesh input through the EVA01 processor and should be used with the OpenEVA runtime/API.

Limitations

The public release focuses on the UND-side path.
This LoRA repo does not include the full Qwen3-VL base weights.
Model quality depends on mesh geometry, scale, topology, materials, and texture availability.
Outputs may contain incorrect or unsupported details when the mesh is ambiguous, incomplete, or visually underspecified.
GPT-ref and GPT-img scores are judge references and are not bitwise reproducible across judge model or API changes.

Citation

bibtex

@misc{eva01_2026,  title  = {EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers},  author = {Zongyuan Yang and Mingjing Yi and Wanli Ma and Chenzhuo Fan and Bocheng Li and Baolin Liu and Yuke Lou and Yingde Song and Yongping Xiong and Zhengdong Guo and Shimu Wang},  year   = {2026},  eprint = {2605.16745},  archivePrefix = {arXiv},  primaryClass = {cs.CV}}

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Model Details

Model Provider

SEELE-AI

Model Tree

Base

Qwen/Qwen3-VL-2B-Instruct

Adapter

this model

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README

License: apache-2.0

Model Details

Table with columns: Item, Description
Item	Description
Model	`SEELE-AI/EVA01-2B-Instruct-LoRA`
Release type	UND-side LoRA adapter
Base model	`Qwen/Qwen3-VL-2B-Instruct`
3D input	`.glb` mesh input through the EVA01 mesh UND processor
Components	PEFT adapter, EVA01 mesh UND encoder, connector, tokenizer, processor, and config files
Training recipe	Alignment followed by instruction tuning
Code	SeeleAI/OpenEVA
Project page	EVA01
Paper	arXiv:2605.16745

Installation

EVA01 requires the OpenEVA code package in addition to the checkpoint files.

bash

git clone https://github.com/SeeleAI/OpenEVA.gitcd OpenEVA/EVA01bash install.shsource .venv/bin/activate

git clone https://github.com/SeeleAI/OpenEVA.gitcd OpenEVA/EVA01bash install.shsource .venv/bin/activate

If a different CUDA wheel index is required, set TORCH_INDEX_URL before running the install script.

bash

TORCH_INDEX_URL=https://download.pytorch.org/whl/cu121 bash install.sh

Quick Inference

CLI:

bash

python infer.py \  --checkpoint SEELE-AI/EVA01-2B-Instruct-LoRA \  --mesh assets/examples/construction_backhoe.glb \  --question "Describe this 3D object in detail."

python infer.py \  --checkpoint SEELE-AI/EVA01-2B-Instruct-LoRA \  --mesh assets/examples/construction_backhoe.glb \  --question "Describe this 3D object in detail."

Python API:

python

import torchfrom eva01 import EVA01ForConditionalGeneration, EVA01Processor model = EVA01ForConditionalGeneration.from_pretrained(    "SEELE-AI/EVA01-2B-Instruct-LoRA",    torch_dtype=torch.bfloat16,    device_map="auto",)processor = EVA01Processor.from_pretrained("SEELE-AI/EVA01-2B-Instruct-LoRA") messages = [{    "role": "user",    "content": [        {"type": "mesh", "mesh": "assets/examples/construction_backhoe.glb"},        {"type": "text", "text": "Describe this 3D object in detail."},    ],}] inputs = processor.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_dict=True,    return_tensors="pt",).to(model.device) output_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)text = processor.batch_decode(    output_ids[:, inputs.input_ids.shape[1]:],    skip_special_tokens=True,    clean_up_tokenization_spaces=False,)[0]print(text)

import torchfrom eva01 import EVA01ForConditionalGeneration, EVA01Processor model = EVA01ForConditionalGeneration.from_pretrained(    "SEELE-AI/EVA01-2B-Instruct-LoRA",    torch_dtype=torch.bfloat16,    device_map="auto",)processor = EVA01Processor.from_pretrained("SEELE-AI/EVA01-2B-Instruct-LoRA") messages = [{    "role": "user",    "content": [        {"type": "mesh", "mesh": "assets/examples/construction_backhoe.glb"},        {"type": "text", "text": "Describe this 3D object in detail."},    ],}] inputs = processor.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_dict=True,    return_tensors="pt",).to(model.device) output_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)text = processor.batch_decode(    output_ids[:, inputs.input_ids.shape[1]:],    skip_special_tokens=True,    clean_up_tokenization_spaces=False,)[0]print(text)

Local base override:

python

model = EVA01ForConditionalGeneration.from_pretrained(    "SEELE-AI/EVA01-2B-Instruct-LoRA",    base_model_name_or_path="/path/to/Qwen3-VL-2B-Instruct",    torch_dtype=torch.bfloat16,    device_map="auto",)

model = EVA01ForConditionalGeneration.from_pretrained(    "SEELE-AI/EVA01-2B-Instruct-LoRA",    base_model_name_or_path="/path/to/Qwen3-VL-2B-Instruct",    torch_dtype=torch.bfloat16,    device_map="auto",)

The public mesh token is <|mesh_und_pad|>. The processor returns input_ids, attention_mask, and mesh_und_values.

Gradio Chat

bash

python app.py --checkpoint SEELE-AI/EVA01-2B-Instruct-LoRA --host 127.0.0.1 --port 7860

python app.py --checkpoint SEELE-AI/EVA01-2B-Instruct-LoRA --host 127.0.0.1 --port 7860

The OpenEVA app supports uploaded .glb files and includes 10 built-in TexVerse examples. The OpenEVA GitHub repository also contains a PBR-rendered example gallery.

PointLLM-200 Evaluation

The OpenEVA eval script downloads PointLLM-200 benchmark files staged in the Full checkpoint repo and writes results under EVA01/outputs/pointllm200/.

bash

python eval_pointllm200.py --variant lora

The PointLLM-200 benchmark source is cited as RunsenXu/PointLLM.

Metrics

All metrics below are reported on PointLLM-200 with 200 samples, fixed seed 20260615, and greedy decoding. GPT-ref and GPT-img are judge metrics and may vary with judge model and API settings.

Table with columns: Model, B-1, B-4, R-L, METEOR, SBERT, SimCSE, GPT-ref, GPT-img
Model	B-1	B-4	R-L	METEOR	SBERT	SimCSE	GPT-ref	GPT-img
PointLLM-13B	7.873	0.649	10.519	13.620	47.539	48.602	51.735	49.745
ShapeLLM-13B	10.542	1.050	12.954	14.234	39.935	40.728	33.925	35.870
ShapeLLM-Omni	11.326	1.197	14.190	13.276	34.617	35.115	25.625	20.190
EVA01-2B-Instruct	6.386	0.589	9.443	13.505	50.651	50.767	59.045	70.335
EVA01-2B-Instruct-LoRA	6.372	0.607	9.455	13.567	51.194	51.320	59.560	71.480

Intended Use

Limitations

The public release focuses on the UND-side path.
This LoRA repo does not include the full Qwen3-VL base weights.
Model quality depends on mesh geometry, scale, topology, materials, and texture availability.
Outputs may contain incorrect or unsupported details when the mesh is ambiguous, incomplete, or visually underspecified.
GPT-ref and GPT-img scores are judge references and are not bitwise reproducible across judge model or API changes.

Citation

bibtex

@misc{eva01_2026,  title  = {EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers},  author = {Zongyuan Yang and Mingjing Yi and Wanli Ma and Chenzhuo Fan and Bocheng Li and Baolin Liu and Yuke Lou and Yingde Song and Yongping Xiong and Zhengdong Guo and Shimu Wang},  year   = {2026},  eprint = {2605.16745},  archivePrefix = {arXiv},  primaryClass = {cs.CV}}

@misc{eva01_2026,  title  = {EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers},  author = {Zongyuan Yang and Mingjing Yi and Wanli Ma and Chenzhuo Fan and Bocheng Li and Baolin Liu and Yuke Lou and Yingde Song and Yongping Xiong and Zhengdong Guo and Shimu Wang},  year   = {2026},  eprint = {2605.16745},  archivePrefix = {arXiv},  primaryClass = {cs.CV}}