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base_model: Qwen/Qwen3.5-0.8B-Base
library_name: peft
pipeline_tag: text-generation
tags:
- architecture
- voxel
- lora
- transformers
---
# ArchiCell
ArchiCell generates discrete architectural tokens from natural-language descriptions and decodes them into 64 x 64 x 64 voxel buildings.
This repository contains the inference weights for two stages of the ArchiCell pipeline:
- `tokenizer/best.pt`: Stage 1 structure-aware VQ tokenizer checkpoint.
- `lora/`: Stage 2 PEFT LoRA adapter and its tokenizer files for `Qwen/Qwen3.5-0.8B-Base`.
The Qwen base model is not duplicated here. Download it separately from [Qwen/Qwen3.5-0.8B-Base](https://huggingface.co/Qwen/Qwen3.5-0.8B-Base).
## Download
```bash
git lfs install
git clone https://huggingface.co/QiHoaran/ArchiCell weights/ArchiCell
git clone https://huggingface.co/Qwen/Qwen3.5-0.8B-Base models/Qwen3.5-0.8B-Base
```
## Use with the ArchiCell source repository
```bash
python stage_3_inference/infer.py \
--model_dir models/Qwen3.5-0.8B-Base \
--lora_ckpt weights/ArchiCell/lora \
--stage3_checkpoint weights/ArchiCell/tokenizer/best.pt \
--out_dir outputs/my_run \
--save_mode voxel
```
Source code and complete instructions: [QiHoaran/ArchiCell on GitHub](https://github.com/QiHoaran/ArchiCell).
## Weight details
- Base model: `Qwen/Qwen3.5-0.8B-Base`
- LoRA rank: 16
- LoRA alpha: 32
- LoRA target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`
- VQ codebook size: 1024
- Tokenizer input: 7 channels
- Tokenizer latent grid: 8 x 8 x 8
- Tokenizer output voxel grid: 64 x 64 x 64
The LoRA training-state checkpoint is intentionally excluded because it is only required for resuming training, not inference.
## Status and limitations
These are research weights for the ArchiCell V1 pipeline. The Stage 1 checkpoint and Stage 2 adapter are published for inference and reproducibility; broader generalization has not been established.
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