---
library_name: gemma_torch
license: gemma
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
tags:
- pytorch
extra_gated_heading: Access CodeGemma on Hugging Face
extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
  and agree to Google’s usage license. To do this, please ensure you’re logged-in
  to Hugging Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---

# CodeGemma Model Card

> [!IMPORTANT]
>
> This repository corresponds to the CodeGemma 7B IT checkpoint for use with [Gemma PyTorch](https://github.com/google/gemma_pytorch). If you're looking for the `transformers` implementation, or more detailed model card, visit https://huggingface.co/google/codegemma-7b-it.

**Model Page**: [CodeGemma](https://ai.google.dev/gemma/docs/codegemma)

**Resources and Technical Documentation**:

* [Technical Report](https://goo.gle/codegemma)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)

**Terms of Use**: [Terms](https://www.kaggle.com/models/google/codegemma/license/consent/verify/huggingface?returnModelRepoId=google/codegemma-7b-it-pytorch)

**Authors**: Google

# Sample Usage

```python
from gemma.config import GemmaConfig, get_config_for_7b, get_config_for_2b
from gemma.model import GemmaForCausalLM
from gemma.tokenizer import Tokenizer
import contextlib
import os
import torch

VARIANT = "7b-it" 
MACHINE_TYPE = "cpu" 
weights_dir = 'codegemma-7b-it-pytorch' 

@contextlib.contextmanager
def _set_default_tensor_type(dtype: torch.dtype):
  """Sets the default torch dtype to the given dtype."""
  torch.set_default_dtype(dtype)
  yield
  torch.set_default_dtype(torch.float)

model_config = get_config_for_2b() if "2b" in VARIANT else get_config_for_7b()
model_config.tokenizer = os.path.join(weights_dir, "tokenizer.model")

device = torch.device(MACHINE_TYPE)
with _set_default_tensor_type(model_config.get_dtype()):
  model = GemmaForCausalLM(model_config)
  ckpt_path = os.path.join(weights_dir, f'codegemma-{VARIANT}.pt')
  model.load_weights(ckpt_path)
  model = model.to(device).eval()

PROMPT = """<start_of_turn>user
Write a Python function to calculate the nth fibonacci number.<end_of_turn>
<start_of_turn>model
"""

model.generate(
    PROMPT,
    device=device,
    output_len=100,
)
```