---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3.5-27B
pipeline_tag: text-generation
library_name: transformers
tags:
- climate
- wind-energy
- opposition-detection
- discourse-analysis
- classification
- qwen3
- fine-tuned
---
# Windy-Qwen3.5-27B
Fine-tuned **Qwen3.5-27B** for three-level classification of wind-energy opposition discourse:
1. **Detection** — binary: does the text express opposition to wind energy?
2. **Frame** (`N_*`) — the high-level frame, if any (e.g. cost, environmental, military, governance).
3. **Claim** (`C_*`) — the specific claim(s) within that frame.
This is the model that accompanies a forthcoming paper from the **C3DS** group on wind-energy opposition discourse. Until that paper is out, it should be treated as a research preview — the codebook, evaluation set, and inference recipe are stable, but the published methodology paper is in preparation.
This is a **merged** checkpoint: a LoRA adapter (rank 16) trained with reverse-engineered chain-of-thought (RECoT) supervision has been merged back into the base weights for direct loading with `transformers`, vLLM, or any standard inference engine.
## Results
Evaluated on the held-out wind-opposition test set (773 rows, 436 opposition-positive). Compared against the smaller Windy siblings, the joint CARDS+Wind 27B variant, and frontier APIs:
### Detection (binary)
| Metric | Windy-4B | Windy-9B | **Windy-27B** | Windy-27B FP8 | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|---|---|---|---|
| Precision | 0.795 | 0.797 | **0.871** | 0.877 | 0.896 | **0.927** |
| Recall | 0.915 | 0.917 | **0.917** | 0.920 | 0.890 | 0.846 |
| F1 | 0.851 | 0.853 | **0.894** | 0.898 | 0.893 | 0.885 |
### Samples F1 (multi-label frame / claim accuracy)
| View | Windy-4B | Windy-9B | **Windy-27B** | Windy-27B FP8 | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|---|---|---|---|
| Frames — all rows | 0.697 | 0.696 | **0.781** | 0.787 | 0.791 | 0.792 |
| Frames — opposition only | 0.699 | 0.695 | **0.747** | 0.751 | 0.734 | 0.697 |
| Claims — all rows | 0.654 | 0.676 | **0.741** | 0.755 | 0.754 | 0.745 |
| Claims — opposition only | 0.623 | 0.660 | **0.675** | 0.694 | 0.667 | 0.614 |
### Highlights
- **Detection F1 ties Claude Opus 4.7** (0.894 vs 0.893) and beats GPT-5.5 (0.885).
- **Wins frames-opposition-only and claims-opposition-only samples F1** vs both frontier APIs — once a row is recognized as opposition, this model attaches the right frames/claims more reliably than Opus or GPT-5.5.
- Zero parse failures on 773 test items vs 1–2 for the frontier APIs.
- Per-row data: 773 rows, 436 opposition-positive (~56%); detection accuracy 0.873.
## Usage
### With vLLM
```bash
vllm serve C3DS/Windy-Qwen3.5-27B \
--port 8000 \
--max-model-len 4096 \
--dtype bfloat16 \
--enable-prefix-caching \
--served-model-name Windy-Qwen3.5-27B
```
The system prompt the model was trained with (`slim_system_instruction`, with the wind frames/claims codebook inlined) is bundled in this repo as [`wind_prompts.json`](./wind_prompts.json).
```python
import json
from huggingface_hub import hf_hub_download
from openai import OpenAI
prompts = json.load(open(hf_hub_download("C3DS/Windy-Qwen3.5-27B", "wind_prompts.json")))
slim_system_instruction = prompts["slim_system_instruction"]
client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
def classify(text):
resp = client.chat.completions.create(
model="Windy-Qwen3.5-27B",
messages=[
{"role": "system", "content": slim_system_instruction},
{"role": "user", "content": text},
],
temperature=0,
max_tokens=4000,
)
return resp.choices[0].message.content
print(classify("Wind farms are killing local property values and chasing tourists away."))
```
The model produces a reasoning trace inside `…` followed by a YAML block:
```yaml
opposition_detected: true
frames:
- N_2
claims:
- C_5_0
```
To parse: take the content after `` and read the YAML.
For an FP8-quantized variant (~27 GB on disk, slightly stronger than BF16 across the board) see [`C3DS/Windy-Qwen3.5-27B-FP8`](https://huggingface.co/C3DS/Windy-Qwen3.5-27B-FP8).
### 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 wind system prompt alongside an image (e.g. a screenshot of a tweet, news
headline, or protest sign) and the model will run the same three-level
detection / frames / claims classification on the visual input.
Serve vLLM with multimodal flags enabled:
```bash
vllm serve C3DS/Windy-Qwen3.5-27B \
--port 8000 \
--max-model-len 8192 \
--trust-remote-code \
--limit-mm-per-prompt image=4 \
--enable-prefix-caching \
--served-model-name Windy-Qwen3.5-27B
```
```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/Windy-Qwen3.5-27B", "wind_prompts.json")))
slim_system_instruction = prompts["slim_system_instruction"]
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="Windy-Qwen3.5-27B",
messages=[
{"role": "system", "content": slim_system_instruction},
{"role": "user", "content": [
{"type": "text", "text": "Read the image (and any caption below) and classify the wind-opposition framing depicted."},
image_part("screenshot.png"),
{"type": "text", "text": "### Caption:\n"},
]},
],
temperature=0,
max_tokens=4000,
)
print(resp.choices[0].message.content)
```
## Training
- **Base model:** `Qwen/Qwen3.5-27B`
- **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
- **Training data:** RECoT chat messages distilled from a teacher LLM (`claude-opus-4-7`) over an expert-annotated wind-opposition corpus, with synthetic positive / near-miss negative augmentation. Final training set: 726 rows after teacher second-guessing filter; 86-row held-out eval mirror.
- **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=8192`, `adamw_8bit`, `bf16`
- **Hardware:** 1× NVIDIA H200
- **Checkpoint selection:** best via `load_best_model_at_end=True`
## Limitations
- **Forthcoming paper.** The methodology, codebook, and dataset details will be published in a future C3DS paper. Model output and per-claim precision/recall should be interpreted as a research preview.
- **Domain-specific.** Trained on wind-opposition discourse from social media and news; behavior on adjacent topics (solar, nuclear, climate-change-in-general) is uncharacterized.
- **Thinking tokens.** Training used `enable_thinking=True`. Either parse output after ``, or disable thinking at inference via `chat_template_kwargs={"enable_thinking": false}`. Reserve token budget for the reasoning trace before the final YAML block.
- **Detection is high-recall, modest-precision.** The model errs toward calling borderline cases "opposition" — recall 0.917, precision 0.871. If your downstream use needs high precision, consider thresholding on additional signals.
## Related models
- [`C3DS/Windy-Qwen3.5-27B-FP8`](https://huggingface.co/C3DS/Windy-Qwen3.5-27B-FP8) — FP8 quantized variant of this model.
- [`C3DS/CARDS-Wind-Qwen3.6-27B-FP8`](https://huggingface.co/C3DS/CARDS-Wind-Qwen3.6-27B-FP8) — joint single-backbone trained on CARDS + Wind concatenated; one model handles both tasks.
## License
Apache 2.0, inherited from Qwen3.5-27B.