---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3.6-27B
pipeline_tag: text-generation
library_name: transformers
tags:
- climate
- climate-change
- climate-discourse
- classification
- 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. (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).

## Results

Evaluated on the held-out CARDS test set (1,436 samples, Level 1, `min_support ≥ 3`):

| Metric | Qwen3.5-27B | Qwen3.5-27B FT | **Qwen3.6-27B FT** | Claude Opus 4.6 | Claude Opus 4.7 |
|---|---|---|---|---|---|
| Samples F1 | 0.844 | 0.884 | **0.893** | **0.893** | 0.882 |
| Macro F1 | 0.710 | 0.766 | 0.748 | 0.751 | **0.771** |
| Micro F1 | 0.854 | 0.877 | **0.885** | 0.881 | 0.874 |
| Precision | 0.870 | 0.879 | **0.893** | 0.863 | 0.868 |
| Recall | 0.838 | 0.874 | 0.876 | **0.900** | 0.880 |
| Parse failures | 86 / 1436 | 0 / 1436 | 2 / 1436 | 0 / 1436 | 0 / 1436 |

- Ties Claude Opus 4.6 on Samples F1 at L1.
- Wins Micro F1 and Precision at every hierarchy level.
- Trails Claude Opus on Recall and Macro F1 for rare labels.
- Parse failures drop from 6% (plain Qwen3.5-27B) to ~0 with fine-tuning.

## Usage

### With vLLM

```bash
vllm serve C3DS/CARDS-Qwen3.6-27B \
  --port 8000 \
  --max-model-len 4096 \
  --dtype bfloat16 \
  --enable-prefix-caching \
  --served-model-name CARDS-Qwen3.6-27B
```

Then query with any OpenAI-compatible client. The system prompt (`slim_system_instruction`) and the user-message suffix (`cot_trigger`) the model was trained with are bundled in this repo as [`cards_prompts.json`](./cards_prompts.json) — self-contained, with the CARDS taxonomy already inlined.

```python
import json
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"]

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

def classify(text):
    resp = client.chat.completions.create(
        model="CARDS-Qwen3.6-27B",
        messages=[
            {"role": "system", "content": slim_system_instruction},
            {"role": "user",   "content": f"### Text:\n{text}\n\n{cot_trigger}"},
        ],
        temperature=0,
        max_tokens=4000,
    )
    return resp.choices[0].message.content

print(classify("These are only a few renewable energy technologies at work"))
```

The model produces a reasoning trace inside `<think>…</think>` followed by a YAML `categories:` block listing predicted CARDS codes. To parse: take the content after `</think>` and read the `categories:` list.

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`
- **Method:** LoRA (rank 16, α 16, dropout 0) on `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`, then merged into base weights
- **Dataset:** [`C3DS/cards_sft_dataset`](https://huggingface.co/datasets/C3DS/cards_sft_dataset)
- **Framework:** Unsloth + TRL `SFTTrainer`
- **Hyperparameters:** 3 epochs, `per_device_train_batch_size=1`, `gradient_accumulation_steps=8`, `lr=2e-4`, cosine schedule, 10 warmup steps, `max_seq_length=4096`, `adamw_8bit`, `bf16`
- **Hardware:** 1× NVIDIA H200, ~2h 7min wall-clock
- **Checkpoint selection:** best via `load_best_model_at_end=True` (step 600, `eval_loss=0.0905`)
- **Final training loss:** 0.125

## 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.
- **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

```bibtex
@article{cards2pO2025,
  title={Large language model reveals an increase in climate contrarian speech in the United States Congress},
  author={Travis G. Coan and Ranadheer Malla and Mirjam O. Nanko and William Kattrup and J. Timmons Roberts and John Cook and Constantine Boussalis},
  journal={Communications Sustainability},
  year={2025}
}
```

## License

Apache 2.0, inherited from Qwen3.6-27B.