metadata
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
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)