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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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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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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [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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- ## Uses
 
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
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- ### Direct Use
 
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
 
 
 
 
 
 
 
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- ### Downstream Use [optional]
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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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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- ### Results
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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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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **APA:**
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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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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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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  ---
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+ # German R1
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+ ![A German whale](https://huggingface.co/malteos/german-r1/resolve/main/german-whale.png)
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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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+ ![some contex](https://huggingface.co/malteos/german-r1/resolve/main/context.png)
 
 
 
 
 
 
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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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+ ...
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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)