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README.md

@@ -96,9 +96,7 @@ python merge.py --output ./trump-forecaster-merged
 
 This downloads the base model, dequantizes to bf16, applies the LoRA adapter, and saves the merged model.
 
-### Inference with the merged model
-
-With [SGLang](https://github.com/sgl-project/sglang) (recommended for MoE):
+### Inference
 
 ```python
 import sglang as sgl
@@ -111,34 +109,21 @@ engine = sgl.Engine(
     tp_size=2,
 )
 
-prompt = """You are a forecasting expert. Given the question and context below, predict the probability that the answer is "Yes".
+news_context = "... relevant news articles ..."
+
+prompt = f"""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?
 
+Context:
+{news_context}
+
 Respond with your reasoning, then give your final answer as a probability between 0 and 1 inside <answer></answer> tags."""
 
 output = engine.generate(prompt, sampling_params={"max_new_tokens": 4096, "stop": ["</answer>"]})
 print(output["text"])
 ```
 
-Or with transformers:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model = AutoModelForCausalLM.from_pretrained(
-    "./trump-forecaster-merged",
-    torch_dtype="auto",
-    device_map="auto",
-    trust_remote_code=True,
-)
-tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-120b", trust_remote_code=True)
-
-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))
-```
-
 ---
 
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