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library_name: transformers
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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[More Information Needed]
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##
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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datasets:
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- openGPT-X/gsm8kx
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language:
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- de
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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# German R1
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
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**Introducing German-R1. We are so back!!11**
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- 🇩🇪 German R1 is a reasoning model almost equivalent to OpenAI‘s o3 or DeepSeek‘s R1 - but it thinks in German!
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- 🇩🇪 German R1 is so efficient that is was build without any government funding.
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- 🇩🇪 German R1 was only trained with legally imported H100s in less than five minutes.
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- Please do NOT take this too serious.
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## Context
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
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See Linkedin: https://www.linkedin.com/posts/activity-7294337496023269376-ZBkI
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## Usage
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You can run the model as follows:
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```python
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from transformers import pipeline, set_seed
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import json
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set_seed(42)
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pipe = pipeline("text-generation", "malteos/german-r1")
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# from gsm8k test set
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question = "James beschließt, 3-mal pro Woche 3 Sprints zu laufen. Er läuft 60 Meter pro Sprint. Wie viele Meter läuft er insgesamt pro Woche?"
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expected_answer = "540"
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# xml reasoning and answer format
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system_prompt = """
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Antworte auf deutsch und in diesem Format:
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<reasoning>
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</reasoning>
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<answer>
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...
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</answer>
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"""
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messages = [
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{
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"role": "system",
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"content": system_prompt,
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},
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{"role": "user", "content": dataset["question"][3]},
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]
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response = pipe(messages, max_new_tokens=256)
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print(json.dumps(response, indent=4, ensure_ascii=False))
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```
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The output should look like this (correct answer: 540)
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```json
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[
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{
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"generated_text": [
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{
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"role": "system",
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"content": "\nAntworte auf deutsch und in diesem Format:\n<reasoning>\n...\n</reasoning>\n<answer>\n...\n</answer>\n"
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},
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{
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"role": "user",
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"content": "James beschließt, 3-mal pro Woche 3 Sprints zu laufen. Er läuft 60 Meter pro Sprint. Wie viele Meter läuft er insgesamt pro Woche?"
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},
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{
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"role": "assistant",
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"content": "<reasoning>\nJames läuft 3 Sprints pro Tag, wobei jeder Sprint 60 Meter beträgt. Deshalb läuft er pro Tag 3 Sprints * 60 Meter/Sprint = 180 Meter. James läuft 3 Mal pro Woche, also läuft er insgesamt 180 Meter * 3 = 540 Meter pro Woche.\n</reasoning>\n<answer>\n540\n</answer>\n"
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}
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]
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}
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]
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```
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## Training details
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- Base model: Qwen2.5-3B-Instruct
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- Implementation: based on [GRPO demo](https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb) by [will brown](https://x.com/willccbb) + language identification reward
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- Training data: German subset of [openGPT-X/gsm8kx](https://huggingface.co/datasets/openGPT-X/gsm8kx/) (machine translated from the English gsm8k)
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## License
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[Qwen research](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE)
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