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

# Model Card for Model ID

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## Model Details

Tiny `Gemma2ForCausalLM` with randomly-initialized weights for testing. Code to generate:
```py
import torch
from transformers import Gemma2ForCausalLM, Gemma2Config, AutoTokenizer

# Set seed for reproducibility
torch.manual_seed(0)

# Initializing a Gemma2 gemma2-9b style configuration
configuration = Gemma2Config(
  head_dim=16,
  hidden_size=32,
  initializer_range=0.02,
  intermediate_size=64,
  max_position_embeddings=8192,
  model_type="gemma2",
  num_attention_heads=2,
  num_hidden_layers=1,
  num_key_value_heads=2,
  vocab_size=256000,
  attn_implementation='eager',
)

# Initializing a model from the gemma2-9b style configuration
model = Gemma2ForCausalLM(configuration)

# Re-use gemma2 tokenizer
tokenizer = AutoTokenizer.from_pretrained("Xenova/gemma2-tokenizer")

# Upload to the HF Hub
model_id = 'hf-internal-testing/tiny-random-Gemma2ForCausalLM'
model.push_to_hub(model_id)
tokenizer.push_to_hub(model_id)
```

### 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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## Uses

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### Direct Use

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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

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#### Summary



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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).

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