55 lines
2.4 KiB
Markdown
55 lines
2.4 KiB
Markdown
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---
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language:
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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tags:
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- chat
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library_name: transformers
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license: apache-2.0
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---
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# Kimina-Prover-Distill-0.6B
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**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.
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For advanced usage examples, see https://github.com/MoonshotAI/Kimina-Prover-Preview/tree/master/kimina_prover_demo
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# Quick Start with vLLM
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You can easily do inference using vLLM:
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_name = "AI-MO/Kimina-Prover-Distill-0.6B"
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model = LLM(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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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?"
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formal_statement = """import Mathlib
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import Aesop
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set_option maxHeartbeats 0
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open BigOperators Real Nat Topology Rat
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/-- 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.-/
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theorem mathd_algebra_478 (b h v : ℝ) (h₀ : 0 < b ∧ 0 < h ∧ 0 < v) (h₁ : v = 1 / 3 * (b * h))
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(h₂ : b = 30) (h₃ : h = 13 / 2) : v = 65 := by
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"""
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prompt = "Think about and solve the following problem step by step in Lean 4."
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prompt += f"\n# Problem:{problem}"""
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prompt += f"\n# Formal statement:\n```lean4\n{formal_statement}\n```\n"
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messages = [
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{"role": "system", "content": "You are an expert in mathematics and Lean 4."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8096)
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output = model.generate(text, sampling_params=sampling_params)
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output_text = output[0].outputs[0].text
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print(output_text)
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```
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