初始化项目，由ModelHub XC社区提供模型

Model: ahalt/event-attribute-extractor
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+tags:
+- event-data
+- political-science
+- computational-social-science
+---
+
+
+
+# Example usage with vLLM
+
+## Load the model and tokenizer
+
+```
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model = LLM(model="ahalt/event-attribute-extractor",
+            enable_prefix_caching=True,
+            max_model_len=8000,
+            gpu_memory_utilization=0.80)
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+
+
+sampling_params = SamplingParams(
+    temperature=0.5,       # Greedy decoding breaks Qwen
+    top_p=0.8,             # Qwen3 non-thinking recommendation  
+    top_k=20,              # Qwen3 recommendation
+    presence_penalty=1.5,  # Recommended for quantized models
+    min_p=0.0,
+    #guided_decoding=guided_decoding_params, # Optionally, set a JSON schema for contrained decoding
+    max_tokens=1024,
+)
+```
+
+
+## Prompt setup
+
+```
+system_content_short = """Extract political events as JSON.
+
+OUTPUT FORMAT:
+[
+  {
+    "event_type": "EVENT_TYPE",
+    "anchor_quote": "quote from text",
+    "actor": "who performed action OR N/A",
+    "recipient": "who was targeted OR N/A", 
+    "date": "when occurred OR N/A",
+    "location": "where occurred OR N/A"
+  }
+]
+
+Return valid JSON only. Empty array [] if no events."""
+
+
+def make_prompt(doc, event_type, tokenizer):
+    messages = [
+                {"role": "system", "content": system_content_short},
+                {"role": "user", "content": f"## Document: {doc}\n\n## Event Type: {event_type}"},
+            ]
+    prompt = tokenizer.apply_chat_template(
+                messages, 
+                tokenize=False, 
+                add_generation_prompt=True,
+                enable_thinking=False
+            )
+    return prompt
+```
+
+
+## Example usage
+
+```
+text = """KYIV, Ukraine (AP) — Ukraine’s anti-corruption agencies said they had uncovered a major graft scheme involving inflated military procurement contracts, just two days after Ukraine’s parliament voted to restore the agencies’ independence.
+
+In a joint statement published Saturday on social media, the National Anti-Corruption Bureau (NABU) and the Specialized Anti-Corruption Prosecutor’s Office (SAPO) said the suspects had taken bribes in a scheme that used state funds to buy drones and other military equipment at inflated prices.
+
+“The essence of the scheme was to conclude state contracts with supplier companies at deliberately inflated prices,” the statement said, adding that offenders had received kickbacks of up to 30% of the contracts’ value.
+
+event_type = "Investigate, charge, or prosecute"
+
+prompt = make_prompt(text, event_type, tokenizer)
+output = model.generate(prompt, sampling_params=sampling_params)
+response = output[0].outputs[0].text.strip()
+
+[{"event_type": "Investigate, charge, or prosecute",
+   "anchor_quote": "Ukraine\u2019s anti-corruption agencies said they had uncovered a major graft scheme involving inflated military procurement contracts",
+   "actor": "National Anti-Corruption Bureau (NABU); Specialized Anti-Corruption Prosecutor\u2019s Office (SAPO)",
+   "recipient": "suspects involved in the scheme",
+   "date": "Saturday",
+   "location": "Ukraine"}
+```