---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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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.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### 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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**BibTeX:**

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**APA:**

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## Glossary [optional]

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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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## Generation


```python
from transformers import AutoTokenizer
import argparse

model_ids = [
    "Qwen/Qwen2-7B-Instruct",
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "meta-llama/Meta-Llama-3-8B-Instruct",
    "deepseek-ai/DeepSeek-Coder-V2-Instruct",
    "microsoft/Phi-3-mini-128k-instruct",
    "google/gemma-2-9b-it",
    "mistralai/Mistral-7B-Instruct-v0.1",
    "mistralai/Mistral-7B-Instruct-v0.2",
    "mistralai/Mistral-7B-Instruct-v0.3",
]


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--organization", type=str, required=True)
    args = parser.parse_args()
    for model_id in model_ids:
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        tiny_model_name = "tiny-random-" + model_id.split("/")[-1]
        repo_id = f"{args.organization}/{tiny_model_name}"
        print(repo_id)
        # tokenizer.push_to_hub(repo_id)
```