Create README.md

README.md

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+---
+library_name: gguf
+---
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
+    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
+    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
+    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-CODE" style="margin: 2px;">
+    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-MODEL" style="margin: 2px;">
+    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## 1. Introduction
+
+We introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thought process, combined with DeepSeek-V3's step-by-step reasoning, to create an initial cold start for reinforcement learning. This process enables us to integrate both informal and formal mathematical reasoning into a unified model.
+
+<p align="center">
+  <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Prover-V2/blob/main/figures/performance.png?raw=true">
+</p>
+
+## 2. Model Summary
+
+---
+
+**Synthesize Cold-Start Reasoning Data through Recursive Proof Search**
+
+- To construct the cold-start dataset, we develop a simple yet effective pipeline for recursive theorem proving, utilizing DeepSeek-V3 as a unified tool for both subgoal decomposition and formalization. We prompt DeepSeek-V3 to decompose theorems into high-level proof sketches while simultaneously formalizing these proof steps in Lean 4, resulting in a sequence of subgoals.
+
+- We use a smaller 7B model to handle the proof search for each subgoal, thereby reducing the associated computational burden. Once the decomposed steps of a challenging problem are resolved, we pair the complete step-by-step formal proof with the corresponding chain-of-thought from DeepSeek-V3 to create cold-start reasoning data.
+
+---
+
+**Reinforcement Learning with Synthetic Cold-Start Data**
+
+- We curate a subset of challenging problems that remain unsolved by the 7B prover model in an end-to-end manner, but for which all decomposed subgoals have been successfully resolved. By composing the proofs of all subgoals, we construct a complete formal proof for the original problem. This proof is then appended to DeepSeek-V3's chain-of-thought, which outlines the corresponding lemma decomposition, thereby producing a cohesive synthesis of informal reasoning and subsequent formalization.
+
+- After fine-tuning the prover model on the synthetic cold-start data, we perform a reinforcement learning stage to further enhance its ability to bridge informal reasoning with formal proof construction. Following the standard training objective for reasoning models, we use binary correct-or-incorrect feedback as the primary form of reward supervision.
+- The resulting model, DeepSeek-Prover-V2-671B, achieves state-of-the-art performance in neural theorem proving, reaching $88.9$% pass ratio on the MiniF2F-test and solving 49 out of 658 problems from PutnamBench. The proofs generated by DeepSeek-Prover-V2 for the miniF2F dataset are available for download as a [ZIP archive](https://github.com/deepseek-ai/DeepSeek-Prover-V2/blob/master/minif2f-solutions.zip).
+
+---
+
+## 3. ProverBench: Formalization of AIME and Textbook Problems
+
+we introduce ProverBench, a benchmark dataset comprising 325 problems. Of these, 15 are formalized from number theory and algebra questions featured in the recent AIME competitions (AIME 24 and 25), offering authentic high-school competition-level challenges. The remaining 310 problems are drawn from curated textbook examples and educational tutorials, contributing a diverse and pedagogically grounded collection of formalized mathematical problems. This benchmark is designed to enable more comprehensive evaluation across both high-school competition problems and undergraduate-level mathematics.
+
+<div align="center">
+
+| Area                | Count |
+| :---------------------: | :-------: |
+| AIME 24&25          | 15    |
+| Number Theory       | 40    |
+| Elementary Algebra  | 30    |
+| Linear Algebra      | 50    |
+| Abstract Algebra    | 40    |
+| Calculus            | 90    |
+| Real Analysis       | 30    |
+| Complex Analysis    | 10    |
+| Functional Analysis | 10    |
+| Probability         | 10    |
+| Total               | 325   |
+
+</div>
+
+## 4. Model & Dataset Downloads
+
+We release DeepSeek-Prover-V2 in two model sizes: 7B and 671B parameters. DeepSeek-Prover-V2-671B is trained on top of DeepSeek-V3-Base. DeepSeek-Prover-V2-7B is built upon DeepSeek-Prover-V1.5-Base and features an extended context length of up to 32K tokens.
+
+<div align="center">
+
+|            **Model**            |                          **Download**                         |
+| :-----------------------------: | :----------------------------------------------------------: |
+|   DeepSeek-Prover-V2-7B   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-7B) |
+|   DeepSeek-Prover-V2-671B   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-671B) |
+
+</div>
+
+<div align="center">
+
+|            **Dataset**            |                          **Download**                         |
+| :-----------------------------: | :----------------------------------------------------------: |
+|   DeepSeek-ProverBench   | [🤗 HuggingFace](https://huggingface.co/datasets/deepseek-ai/DeepSeek-ProverBench) |
+
+</div>
+
+## 5. Quick Start
+
+You can directly use [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference. DeepSeek-Prover-V2-671B shares the same architecture as DeepSeek-V3. For detailed information and supported features, please refer to [the DeepSeek-V3 documentation on Hugging Face](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/deepseek_v3.md).
+
+The following is a basic example of generating a proof for a problem from the miniF2F dataset:
+````python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+torch.manual_seed(30)
+
+model_id = "DeepSeek-Prover-V2-7B"  # or DeepSeek-Prover-V2-671B
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+formal_statement = """
+import Mathlib
+import Aesop
+
+set_option maxHeartbeats 0
+
+open BigOperators Real Nat Topology Rat
+
+/-- What is the positive difference between $120\%$ of 30 and $130\%$ of 20? Show that it is 10.-/
+theorem mathd_algebra_10 : abs ((120 : ℝ) / 100 * 30 - 130 / 100 * 20) = 10 := by
+  sorry
+""".strip()
+
+prompt = """
+Complete the following Lean 4 code:
+
+```lean4
+{}
+```
+
+Before producing the Lean 4 code to formally prove the given theorem, provide a detailed proof plan outlining the main proof steps and strategies.
+The plan should highlight key ideas, intermediate lemmas, and proof structures that will guide the construction of the final formal proof.
+""".strip()
+
+chat = [
+  {"role": "user", "content": prompt.format(formal_statement)},
+]
+
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
+inputs = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+import time
+start = time.time()
+outputs = model.generate(inputs, max_new_tokens=8192)
+print(tokenizer.batch_decode(outputs))
+print(time.time() - start)
+````
+
+## 6. License
+The use of DeepSeek-Prover-V2 models is subject to [the Model License](LICENSE-MODEL).
+
+## 7. Contact
+
+If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).