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README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- forecasting
+- prediction
+- reinforcement-learning
+- grpo
+- lora
+- mixture-of-experts
+datasets:
+- LightningRodLabs/WWTD-2025
+base_model: openai/gpt-oss-120b
+pipeline_tag: text-generation
+model-index:
+- name: Trump-Forecaster
+  results:
+  - task:
+      type: text-generation
+      name: Probabilistic Forecasting
+    dataset:
+      name: WWTD-2025
+      type: LightningRodLabs/WWTD-2025
+      split: test
+    metrics:
+    - type: brier_score
+      value: 0.194
+      name: Brier Score
+    - type: ece
+      value: 0.079
+      name: Expected Calibration Error
+---
+
+# Trump-Forecaster
+
+**RL-tuned gpt-oss-120b for predicting Trump administration actions. Beats GPT-5 on held-out forecasting questions.**
+
+This model was fine-tuned with reinforcement learning (GRPO) using Brier score as the reward signal, trained on the [WWTD-2025](https://huggingface.co/datasets/LightningRodLabs/WWTD-2025) dataset of 2,108 binary forecasting questions about Trump's actions from January-December 2025.
+
+## Results
+
+Evaluated on 682 held-out test questions (with news context):
+
+| Model | Brier | BSS | ECE |
+|---|---|---|---|
+| **gpt-oss-120b RL (this model)** | **0.194** | **0.16** | **0.079** |
+| GPT-5 | 0.200 | 0.14 | 0.091 |
+| gpt-oss-120b (base) | 0.213 | 0.08 | 0.111 |
+
+Without context (question only):
+
+| Model | Brier | BSS | ECE |
+|---|---|---|---|
+| **gpt-oss-120b RL** | **0.242** | **-0.04** | 0.164 |
+| GPT-5 | 0.258 | -0.11 | 0.191 |
+| gpt-oss-120b (base) | 0.260 | -0.12 | 0.189 |
+
+- **Brier Score**: Mean squared error between predicted probability and outcome (lower = better)
+- **BSS (Brier Skill Score)**: Improvement over base-rate guessing (positive = better than naive)
+- **ECE**: Expected Calibration Error (lower = better calibrated)
+
+## Training
+
+- **Base model**: [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) (120B MoE, 5.1B active params, 128 experts Top-4)
+- **Method**: GRPO with Brier score reward via [Tinker](https://tinker.computer)
+- **LoRA rank**: 32
+- **Learning rate**: 4e-5
+- **Batch size**: 32, group size 8
+- **Training steps**: 50
+- **Max tokens**: 16,384
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "LightningRodLabs/Trump-Forecaster",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("LightningRodLabs/Trump-Forecaster", trust_remote_code=True)
+
+prompt = """You are a forecasting expert. Given the question and context below, predict the probability that the answer is "Yes".
+
+Question: Will Trump impose 25% tariffs on all goods from Canada by February 1, 2025?
+
+Respond with your reasoning, then give your final answer as a probability between 0 and 1 inside <answer></answer> tags."""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=True, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+For faster inference with the MoE architecture, use [SGLang](https://github.com/sgl-project/sglang):
+
+```python
+import sglang as sgl
+
+engine = sgl.Engine(model_path="LightningRodLabs/Trump-Forecaster", trust_remote_code=True, dtype="bfloat16")
+output = engine.generate(prompt, sampling_params={"max_new_tokens": 4096, "stop": ["</answer>"]})
+```
+
+## Dataset
+
+Trained on [LightningRodLabs/WWTD-2025](https://huggingface.co/datasets/LightningRodLabs/WWTD-2025):
+- 2,790 binary forecasting questions about Trump administration actions
+- Auto-generated from news (Jan-Dec 2025) using the [Lightning Rod SDK](https://lightningrod.ai/sdk)
+- Ground-truth labels from web search verification
+- Temporal split: 2,108 train / 682 test (no leakage)
+
+## Links
+
+- Dataset: [LightningRodLabs/WWTD-2025](https://huggingface.co/datasets/LightningRodLabs/WWTD-2025)
+- Training platform: [Tinker](https://tinker.computer)
+- Data generation: [Lightning Rod SDK](https://lightningrod.ai/sdk)