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@@ -61,45 +61,13 @@ The pipeline we used to produce the data and models is fully open-sourced!
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  We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
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  to fully reproduce our results, including data generation.
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- # How to use the models?
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- Our models can be used in 3 inference modes: chain-of-thought (CoT), tool-integrated reasoning (TIR) and generative solution selection (GenSelect).
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-
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- To run inference with CoT mode, you can use this example code snippet.
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-
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- ```python
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- import transformers
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- import torch
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-
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- model_id = "nvidia/OpenMath-Nemotron-14B-Kaggle"
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-
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- pipeline = transformers.pipeline(
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- "text-generation",
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- model=model_id,
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- model_kwargs={"torch_dtype": torch.bfloat16},
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- device_map="auto",
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- )
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-
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- messages = [
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- {
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- "role": "user",
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- "content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.\n\n" +
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- "What is the minimum value of $a^2+6a-7$?"},
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- ]
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-
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- outputs = pipeline(
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- messages,
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- max_new_tokens=4096,
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- )
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- print(outputs[0]["generated_text"][-1]['content'])
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- ```
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-
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- To run inference with TIR or GenSelect modes, we highly recommend to use our
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  [reference implementation in NeMo-Skills](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/evaluation/).
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  Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain.
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-
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  ## Citation
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  If you find our work useful, please consider citing us!
@@ -134,7 +102,7 @@ This model is intended to facilitate research in the area of mathematical reason
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  Huggingface 04/23/2025 <br>
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- ## Model Architecture: <br>
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  **Architecture Type:** Transformer decoder-only language model  <br>
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  ** This model has 1.5B of model parameters. <br>
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- ## Input: <br>
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  **Input Type(s):** Text <br>
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@@ -157,7 +125,7 @@ Huggingface 04/23/2025 <br>
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- ## Output: <br>
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  **Output Type(s):** Text <br>
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- ## Software Integration : <br>
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  **Runtime Engine(s):** <br>
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@@ -195,7 +163,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
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- ## Model Version(s):
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  [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B)
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  We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
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  to fully reproduce our results, including data generation.
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+ ## How to use the models?
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+ This model will always use code execution to solve math problems, so we highly recommend to run inference with our
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  [reference implementation in NeMo-Skills](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/evaluation/).
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  Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain.
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  ## Citation
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  If you find our work useful, please consider citing us!
 
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  Huggingface 04/23/2025 <br>
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+ ### Model Architecture: <br>
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  **Architecture Type:** Transformer decoder-only language model  <br>
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  ** This model has 1.5B of model parameters. <br>
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+ ### Input: <br>
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  **Input Type(s):** Text <br>
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+ ### Output: <br>
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  **Output Type(s):** Text <br>
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+ ### Software Integration : <br>
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  **Runtime Engine(s):** <br>
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+ ### Model Version(s):
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  [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B)
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