初始化项目，由ModelHub XC社区提供模型

Model: AI-MO/Kimina-Prover-Distill-0.6B
Source: Original Platform

README.md

@@ -0,0 +1,55 @@
+---
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+tags:
+- chat
+library_name: transformers
+license: apache-2.0
+---
+# Kimina-Prover-Distill-0.6B
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66775fab8c58c5a12ae629ac/mtT1qq2bKycrWvy9NT0ly.png)
+
+**AI-MO/Kimina-Prover-Distill-0.6B** is a theorem proving model developed by Project Numina and Kimi teams, focusing on competition style problem solving capabilities in Lean 4. It is a distillation of **Kimina-Prover-72B**, a model trained via large scale reinforcement learning. It achieves 68.85% accuracy with Pass@32 on MiniF2F-test.
+
+For advanced usage examples, see https://github.com/MoonshotAI/Kimina-Prover-Preview/tree/master/kimina_prover_demo
+
+# Quick Start with vLLM
+
+You can easily do inference using vLLM:
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+model_name = "AI-MO/Kimina-Prover-Distill-0.6B"
+model = LLM(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+problem = "The volume of a cone is given by the formula $V = \frac{1}{3}Bh$, where $B$ is the area of the base and $h$ is the height. The area of the base of a cone is 30 square units, and its height is 6.5 units. What is the number of cubic units in its volume?"
+formal_statement = """import Mathlib
+import Aesop
+set_option maxHeartbeats 0
+open BigOperators Real Nat Topology Rat
+/-- The volume of a cone is given by the formula $V = \frac{1}{3}Bh$, where $B$ is the area of the base and $h$ is the height. The area of the base of a cone is 30 square units, and its height is 6.5 units. What is the number of cubic units in its volume? Show that it is 65.-/
+theorem mathd_algebra_478 (b h v : ℝ) (h₀ : 0 < b ∧ 0 < h ∧ 0 < v) (h₁ : v = 1 / 3 * (b * h))
+    (h₂ : b = 30) (h₃ : h = 13 / 2) : v = 65 := by
+"""
+prompt = "Think about and solve the following problem step by step in Lean 4."
+prompt += f"\n# Problem:{problem}"""
+prompt += f"\n# Formal statement:\n```lean4\n{formal_statement}\n```\n"
+messages = [
+    {"role": "system", "content": "You are an expert in mathematics and Lean 4."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8096)
+output = model.generate(text, sampling_params=sampling_params)
+output_text = output[0].outputs[0].text
+print(output_text)
+```