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

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Code used to generate the model:
```py
from transformers import Phi3Config, Phi3ForCausalLM, AutoTokenizer

model = Phi3ForCausalLM(Phi3Config(
    hidden_size=32,
    intermediate_size=64,
    num_attention_heads=4,
    num_hidden_layers=2,
    num_key_value_heads=4,
    pad_token_id=32000,
    sliding_window=2047,
))
tokenizer = AutoTokenizer.from_pretrained('microsoft/Phi-3-mini-4k-instruct')

model.push_to_hub('Xenova/tiny-random-Phi3ForCausalLM')
tokenizer.push_to_hub('Xenova/tiny-random-Phi3ForCausalLM')
```


## 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.

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### Model Sources [optional]

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## 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

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#### 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]

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## Environmental Impact

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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).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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