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  1. README.md +7 -7
README.md CHANGED
@@ -10,7 +10,7 @@ tags:
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  - instruct
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  base_model: Qwen/Qwen3-4B-Instruct-2507
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  datasets:
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- - data-agents/jupyter-agent-dataset
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  language:
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  - en
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  - code
@@ -50,7 +50,7 @@ On the [DABStep benchmark](https://huggingface.co/spaces/adyen/DABstep) for data
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  ## Model Sources
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  - **Repository:** [jupyter-agent](https://github.com/huggingface/jupyter-agent)
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- - **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/data-agents/jupyter-agent-dataset)
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  - **Blog post:** [Jupyter Agents: training LLMs to reason with notebooks](https://huggingface.co/blog/jupyter-agent-2)
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  - **Demo:** [Jupyter Agent 2](https://huggingface.co/spaces/lvwerra/jupyter-agent-2)
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@@ -61,7 +61,7 @@ On the [DABStep benchmark](https://huggingface.co/spaces/adyen/DABstep) for data
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- model_name = "data-agents/jupyter-agent-qwen3-4b-instruct"
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  # Load model and tokenizer
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -157,7 +157,7 @@ messages = [
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  ### Training Data
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- The model was fine-tuned on the [Jupyter Agent Dataset](https://huggingface.co/datasets/data-agents/jupyter-agent-dataset), which contains:
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  - **51,389 synthetic notebooks** (~0.2B tokens, total 1B tokens)
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  - **Dataset-grounded QA pairs** from real Kaggle notebooks
@@ -238,14 +238,14 @@ We can also see, that the hard score can increase too even though our dataset is
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  author={Baptiste Colle and Hanna Yukhymenko and Leandro von Werra},
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  year={2025},
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  publisher={Hugging Face},
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- url={https://huggingface.co/data-agents/jupyter-agent-qwen3-4b-instruct}
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  }
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  ```
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  ## Related Work
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- - **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/data-agents/jupyter-agent-dataset)
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- - **Thinking version:** [jupyter-agent-qwen3-4b-thinking](https://huggingface.co/data-agents/jupyter-agent-qwen3-4b-thinking)
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  - **Base model:** [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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  - **Benchmark:** [DABStep](https://huggingface.co/spaces/adyen/DABstep)
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10
  - instruct
11
  base_model: Qwen/Qwen3-4B-Instruct-2507
12
  datasets:
13
+ - jupyter-agent/jupyter-agent-dataset
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  language:
15
  - en
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  - code
 
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  ## Model Sources
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  - **Repository:** [jupyter-agent](https://github.com/huggingface/jupyter-agent)
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+ - **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
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  - **Blog post:** [Jupyter Agents: training LLMs to reason with notebooks](https://huggingface.co/blog/jupyter-agent-2)
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  - **Demo:** [Jupyter Agent 2](https://huggingface.co/spaces/lvwerra/jupyter-agent-2)
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model_name = "jupyter-agent/jupyter-agent-qwen3-4b-instruct"
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  # Load model and tokenizer
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
 
157
 
158
  ### Training Data
159
 
160
+ The model was fine-tuned on the [Jupyter Agent Dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset), which contains:
161
 
162
  - **51,389 synthetic notebooks** (~0.2B tokens, total 1B tokens)
163
  - **Dataset-grounded QA pairs** from real Kaggle notebooks
 
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  author={Baptiste Colle and Hanna Yukhymenko and Leandro von Werra},
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  year={2025},
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  publisher={Hugging Face},
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+ url={https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-instruct}
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  }
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  ```
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  ## Related Work
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+ - **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
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+ - **Thinking version:** [jupyter-agent-qwen3-4b-thinking](https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-thinking)
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  - **Base model:** [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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  - **Benchmark:** [DABStep](https://huggingface.co/spaces/adyen/DABstep)
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