Update README.md

README.md

@@ -5,8 +5,12 @@ pipeline_tag: text-generation
 language:
   - en
 tags:
+  - lora
+  - peft
+  - adapter
   - safety
   - alignment
+  - jailbreak-robustness
 base_model:
   - meta-llama/Llama-3.1-8B-Instruct
   - Qwen/Qwen2.5-7B-Instruct
@@ -15,14 +19,15 @@ base_model_relation: adapter
 <h1 align="center">HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment</h1>
 <p align="center">
   <a href="https://arxiv.org/abs/2607.00572"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b.svg?logo=arxiv&logoColor=white" alt="Paper"></a>
-  <a href="https://huggingface.co/microsoft/HARC"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow.svg" alt="Hugging Face"></a>
+  <a href="https://huggingface.co/collections/microsoft/harc"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Collection-HARC-ff9d00.svg" alt="Collection"></a>
   <a href="https://github.com/microsoft/HARC"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg?logo=github" alt="GitHub"></a>
-  <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"></a>
+  <a href="https://huggingface.co/microsoft/HARC/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"></a>
 </p>
 
 ---
 
 ## Model description
+
 HARC couples a model's internal *harmfulness* and *refusal* directions at both
 prompt-side and response-side token positions, using an additive margin-hinge
 loss on cosine projections of the residual stream. The intervention is confined
@@ -31,47 +36,54 @@ layers, which improves robustness to jailbreak attacks while preserving general
 capability and avoiding the over-refusal regression typical of broader safety
 tuning.
 
-We provide HARC adapters in this repository. The adapter is applied to attention and MLP projections
-and trained with a composite objective:
-**(i)** the margin-hinge coupling loss
-**(ii)** a KL-divergence retention term anchoring benign outputs to the base model
-**(iii)** a cross-entropy term supervising refusal text on harmful prompts.
+**This repository contains the HARC LoRA adapters.** The adapter is applied to attention and MLP projections
+and trained with a composite objective: **(i)** the margin-hinge coupling loss **(ii)** a KL-divergence retention term anchoring benign outputs to the base model **(iii)** a cross-entropy term supervising refusal text on harmful prompts.
 Training directions are extracted via difference-of-means on contrastive prompt sets
 and periodically recomputed with EMA blending. The adapter adds ~1% trainable parameters and leaves the base
 architecture unchanged.
 
-
 - **Backbone models:** Llama-3.1-8B-Instruct, Qwen-2.5-7B-Instruct
+- **Collection:** [HARC Collection](https://huggingface.co/collections/microsoft/harc)
 - **Paper:** [arXiv:2607.00572](https://arxiv.org/abs/2607.00572)
 - **Code:** [github.com/microsoft/HARC](https://github.com/microsoft/HARC)
 
+## The HARC collection
+
+| Repo | Contents | License |
+|---|---|---|
+| **microsoft/HARC** (this repo) | LoRA adapters for both backbones | MIT |
+| [microsoft/HARC-Llama-3.1-8B-Instruct](https://huggingface.co/microsoft/HARC-Llama-3.1-8B-Instruct) | Merged full model | Llama 3.1 Community License |
+| [microsoft/HARC-Qwen2.5-7B-Instruct](https://huggingface.co/microsoft/HARC-Qwen2.5-7B-Instruct) | Merged full model | Apache-2.0 |
+
+Use this repo if you want the lightweight adapters to load on top of your own
+copy of the base model; use the merged-model repos if you want a single
+ready-to-run checkpoint.
+
 ## Repository structure
-Each model is provided in two forms: the standalone LoRA adapter and a pre-merged
-full model (adapter fused into the base weights).
 
 ```
 microsoft/HARC/
-├── adapters/
-│   ├── harc_llama3.1_8b/   
-│   └── harc_qwen2.5_7b/    
-└── models/
-    ├── harc_llama3.1_8b/   
-    └── harc_qwen2.5_7b/    
+└── adapters/
+    ├── harc_llama3.1_8b/    # base = Llama-3.1-8B-Instruct
+    └── harc_qwen2.5_7b/     # base = Qwen2.5-7B-Instruct
 ```
 
 ## How to use
+
 Use the base model's standard chat template in both cases.
 
 ### Option A — pre-merged full model (simplest)
 
+Loads directly from the merged-model repo; no base download or PEFT required.
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-repo = "microsoft/HARC"
-subfolder = "models/harc_qwen2.5_7b"          # or "models/harc_llama3.1_8b"
+# pick the merged model you want
+repo = "microsoft/HARC-Qwen2.5-7B-Instruct"   # or "microsoft/HARC-Llama-3.1-8B-Instruct"
 
-tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=subfolder)
-model = AutoModelForCausalLM.from_pretrained(repo, subfolder=subfolder, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
 
 messages = [{"role": "user", "content": "Hello!"}]
 inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
@@ -81,6 +93,8 @@ print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
 
 ### Option B — base model + LoRA adapter (via PEFT)
 
+Load the base model, then attach the adapter from this repo with the matching `subfolder`.
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
@@ -98,14 +112,18 @@ hardware requirements match the base model (a 7–8B model in bf16/fp16 fits on
 24GB GPU).
 
 ## Results
-<p align="center">
-  <img src="assets/HARC-res.png" alt="HARC main results on Llama-3.1-8B and Qwen-2.5-7B" width="60%">
-</p>
 
-## Results
-This project is licensed under the MIT License.
+![HARC main results on Llama-3.1-8B and Qwen-2.5-7B](https://huggingface.co/microsoft/HARC/resolve/main/assets/HARC-res.png)
+
+## License
+
+The LoRA adapters in this repository are released under the MIT License. The
+merged full models are distributed in separate repositories under their base
+model's license: the Llama variant under the Meta Llama 3.1 Community License,
+and the Qwen variant under Apache-2.0.
 
 ## Citation
+
 ```bibtex
 @article{chua2026harc,
   title={HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment},