Update README.md

README.md

@@ -1,12 +1,50 @@
 ---
 license: apache-2.0
 language:
-- en
-base_model:
-- Qwen/Qwen3-4B-Instruct-2507
+  - en
+base_model: Qwen/Qwen3-4B-Instruct-2507
 pipeline_tag: text-generation
+library_name: peft
 tags:
-- lora
-- peft
-- qwen3
----
+  - lora
+  - peft
+  - qwen3
+  - floorplan
+  - hypergraph
+---
+
+# HypergraphFormer
+
+LoRA adapters fine-tuning **Qwen/Qwen3-4B-Instruct-2507** for hypergraph-based
+floorplan generation (RPLAN). 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)