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1
reasoning math proof

DeepSeek-Prover-V2 is 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 Deep

2
model reason

Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of

3
model reason

Qwen3 14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Nu

4
model reason

Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of La

5
model reason

Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Lay

6
model reason

Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of L

7
model reason

Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of

Top Rated

1
model reason

Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of

2
reasoning math proof

DeepSeek-Prover-V2 is 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 Deep

3
model reason

Qwen3 14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Nu

4
model reason

Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of La

5
model reason

Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Lay

6
model reason

Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of L

7
model reason

Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of

REASON

1
reasoning math proof

DeepSeek-Prover-V2 is 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 Deep

2
model reason

Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of

3
model reason

Qwen3 14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Nu

4
model reason

Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of La

5
model reason

Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Lay

6
model reason

Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of L

7
model reason

Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of

Reviews

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  • A
    aigc_coder 2025-05-02 12:03
    5.0
    Interesting:5,Helpfulness:5,Correctness:5

    Qwen3 32B model series are the most widely adopted and deployed model in industrial applications, which compromise of inference speed and performance. This updated version of Qwen3 32B model have the thinking mode and non-thinking mode, which supports both the common task of chat/text generation and more complex task of math, code generation, etc. On the AIME and many other math benchmarks, Qwen3 surpass many of the opensource counterpart.

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  • H
    HaoZLi 2025-05-01 09:48
    4.0
    Interesting:4,Helpfulness:3,Correctness:4
    Prompt: How to use KL divergence to help regularize the RL training of large reasoning model? What's the drawback of current RL algorithm?

    There is not public access to test the prover model. And I tried to use a previous prompt in machine learning to ask DeepSeek model to make a proof. But it seems like the question is over simplified and it only gave some introductory summarization. But the thinking process is quite interesting.

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