Update model card: fix paper year, drop English-only, add multimodal section

README.md

@@ -14,13 +14,16 @@ tags:
 - qwen3
 - fine-tuned
 - cards
+- image-text-to-text
+- multimodal
+- vision-language
 datasets:
 - C3DS/cards_sft_dataset
 ---
 
 # CARDS-Qwen3.6-27B
 
-Fine-tuned **Qwen3.6-27B** for classification of climate-contrarian claims using the **CARDS taxonomy** from Coan et al. (2026).
+Fine-tuned **Qwen3.6-27B** for classification of climate-contrarian claims using the **CARDS taxonomy** from Coan et al. (2025).
 
 This is a **merged** checkpoint: a LoRA adapter (rank 16) trained on the CARDS SFT dataset has been merged back into the base weights for direct loading with `transformers`, vLLM, or any standard inference engine. The separate adapter is available at [`C3DS/CARDS-Qwen3.6-27B-lora`](https://huggingface.co/C3DS/CARDS-Qwen3.6-27B-lora).
 
@@ -87,6 +90,59 @@ The model produces a reasoning trace inside `<think>…</think>` followed by a Y
 
 See the [project repository](https://github.com/project-c3ds/cards-2pO-paper) for training scripts, evaluation code, and dataset preparation.
 
+### Multimodal — image + text
+
+The base Qwen3.5/3.6 family supports image inputs via the OpenAI-compatible
+`image_url` content part, and this fine-tune preserves that capability — pass
+the system prompt below alongside an image (with or without caption text) and
+the model will classify the depicted claim under the CARDS taxonomy.
+
+Serve vLLM with multimodal flags enabled:
+
+```bash
+vllm serve C3DS/CARDS-Qwen3.6-27B \
+  --port 8000 \
+  --max-model-len 8192 \
+  --trust-remote-code \
+  --limit-mm-per-prompt image=4 \
+  --enable-prefix-caching \
+  --served-model-name CARDS-Qwen3.6-27B
+```
+
+```python
+import base64, json, mimetypes
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+from openai import OpenAI
+
+prompts = json.load(open(hf_hub_download("C3DS/CARDS-Qwen3.6-27B", "cards_prompts.json")))
+slim_system_instruction = prompts["slim_system_instruction"]
+cot_trigger             = prompts["cot_trigger"]
+
+def image_part(path):
+    p = Path(path)
+    mime = mimetypes.guess_type(p)[0] or "image/png"
+    b64 = base64.b64encode(p.read_bytes()).decode()
+    return {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}}
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
+
+resp = client.chat.completions.create(
+    model="CARDS-Qwen3.6-27B",
+    messages=[
+        {"role": "system", "content": slim_system_instruction},
+        {"role": "user", "content": [
+            {"type": "text", "text": "Read the image (and any caption below) and classify the climate claim it makes."},
+            image_part("screenshot.png"),
+            {"type": "text", "text": f"### Caption:\n<optional caption>\n\n{cot_trigger}"},
+        ]},
+    ],
+    temperature=0,
+    max_tokens=4000,
+)
+print(resp.choices[0].message.content)
+```
+
 ## Training
 
 - **Base model:** `Qwen/Qwen3.6-27B`
@@ -101,7 +157,6 @@ See the [project repository](https://github.com/project-c3ds/cards-2pO-paper) fo
 ## Limitations
 
 - **Macro F1 on rare labels.** The model trails Claude Opus at L3 macro-F1 (0.483 vs 0.531), reflecting the long-tailed label distribution of CARDS.
-- **English only.**
 - **Thinking tokens.** Training used `enable_thinking=True`. Either parse output after `</think>`, or disable thinking at inference via `chat_template_kwargs={"enable_thinking": false}`. Reserve token budget for the reasoning trace before the final YAML block.
 
 ## Citation