# Source: https://huggingface.co/docs/transformers/v5.0.0rc1/main_classes/executorch.md

# Source: https://huggingface.co/docs/transformers/v5.0.0rc1/executorch.md

# Source: https://huggingface.co/docs/transformers/v5.0.0rc1/main_classes/executorch.md

# Source: https://huggingface.co/docs/transformers/v5.0.0rc1/executorch.md

# Source: https://huggingface.co/docs/transformers/v4.57.3/executorch.md

# ExecuTorch

[ExecuTorch](https://pytorch.org/executorch/stable/index.html) is a platform that enables PyTorch training and inference programs to be run on mobile and edge devices. It is powered by [torch.compile](https://pytorch.org/docs/stable/torch.compiler.html) and [torch.export](https://pytorch.org/docs/main/export.html) for performance and deployment.

You can use ExecuTorch with Transformers with [torch.export](https://pytorch.org/docs/main/export.html). The [convert_and_export_with_cache()](/docs/transformers/v4.57.3/en/main_classes/executorch#transformers.convert_and_export_with_cache) method converts a [PreTrainedModel](/docs/transformers/v4.57.3/en/main_classes/model#transformers.PreTrainedModel) into an exportable module. Under the hood, it uses [torch.export](https://pytorch.org/docs/main/export.html) to export the model, ensuring compatibility with ExecuTorch.

```py
import torch
from transformers import LlamaForCausalLM, AutoTokenizer, GenerationConfig
from transformers.integrations.executorch import(
    TorchExportableModuleWithStaticCache,
    convert_and_export_with_cache
)

generation_config = GenerationConfig(
    use_cache=True,
    cache_implementation="static",
    cache_config={
        "batch_size": 1,
        "max_cache_len": 20,
    }
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B", pad_token="", padding_side="right")
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", device_map="auto", dtype=torch.bfloat16, attn_implementation="sdpa", generation_config=generation_config)

exported_program = convert_and_export_with_cache(model)
```

The exported PyTorch model is now ready to be used with ExecuTorch. Wrap the model with [TorchExportableModuleWithStaticCache](/docs/transformers/v4.57.3/en/main_classes/executorch#transformers.TorchExportableModuleWithStaticCache) to generate text.

```py
prompts = ["Simply put, the theory of relativity states that "]
prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
prompt_token_ids = prompt_tokens["input_ids"]

generated_ids = TorchExportableModuleWithStaticCache.generate(
    exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=20,
)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text)
['Simply put, the theory of relativity states that 1) the speed of light is the']
```