| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: Qwen/Qwen3-4B-Instruct-2507 |
| pipeline_tag: text-generation |
| library_name: peft |
| tags: |
| - lora |
| - peft |
| - qwen3 |
| - floorplan |
| - hypergraph |
| --- |
| |
| # HypergraphFormer |
|
|
| Link to paper: https://arxiv.org/abs/2605.18932 |
| LoRA adapters fine-tuning **Qwen/Qwen3-4B-Instruct-2507** for hypergraph-based |
| floorplan generation. The repo contains several adapters trained on |
| different dataset sizes. |
|
|
| ## Checkpoints |
|
|
| | Subfolder | Train samples | Step | |
| |---|---|---| |
| | `qwen_hypergraphformer_1000_samples/checkpoint-240` | 1,000 | 240 | |
| | `qwen_hypergraphformer_5000_samples/checkpoint-750` | 5,000 | 750 | |
| | `qwen_hypergraphformer_10000_samples/checkpoint-1500`| 10,000 | 1500 | |
| | `qwen_hypergraphformer_25000_samples/checkpoint-3900`| 25,000 | 3900 | |
| | `qwen_hypergraphformer/checkpoint-8700` | full | 8700 | |
|
|
| ## LoRA configuration |
|
|
| - Rank `r = 64`, `lora_alpha = 128`, `lora_dropout = 0.1` |
| - Target modules: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
| - Task: `CAUSAL_LM` |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| base_id = "Qwen/Qwen3-4B-Instruct-2507" |
| repo_id = "NikitaKlimenko/HypergraphFormer" |
| subfolder = "qwen_hypergraphformer_25000_samples/checkpoint-3900" |
| |
| tok = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder) |
| base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto") |
| model = PeftModel.from_pretrained(base, repo_id, subfolder=subfolder) |
| |
