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

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

# Generation strategies

A decoding strategy informs how a model should select the next generated token. There are many types of decoding strategies, and choosing the appropriate one has a significant impact on the quality of the generated text.

This guide will help you understand the different decoding strategies available in Transformers and how and when to use them.

## Basic decoding methods

These are well established decoding methods, and should be your starting point for text generation tasks.

### Greedy search

Greedy search is the default decoding strategy. It selects the next most likely token at each step. Unless specified in [GenerationConfig](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationConfig), this strategy generates a maximum of 20 new tokens.

Greedy search works well for tasks with relatively short outputs where creativity is not a priority. However, it breaks down when generating longer sequences because it begins to repeat itself.

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to(device)

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", dtype=torch.float16).to(device)
# explicitly set to default length because Llama2 generation length is 4096
outputs = model.generate(**inputs, max_new_tokens=20)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
'Hugging Face is an open-source company that provides a suite of tools and services for building, deploying, and maintaining natural language processing'
```

### Sampling

Sampling, or multinomial sampling, randomly selects a token based on the probability distribution over the entire model's vocabulary (as opposed to the most likely token, as in greedy search). This means every token with a non-zero probability has a chance to be selected. Sampling strategies reduce repetition and can generate more creative and diverse outputs.

Enable multinomial sampling with `do_sample=True` and `num_beams=1`.

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to(device)

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", dtype=torch.float16).to(device)
# explicitly set to 100 because Llama2 generation length is 4096
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, num_beams=1)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
'Hugging Face is an open-source company 🤗\nWe are open-source and believe that open-source is the best way to build technology. Our mission is to make AI accessible to everyone, and we believe that open-source is the best way to achieve that.'
```

### Beam search

Beam search keeps track of several generated sequences (beams) at each time step. After a certain number of steps, it selects the sequence with the highest *overall* probability. Unlike greedy search, this strategy can "look ahead" and pick a sequence with a higher probability overall even if the initial tokens have a lower probability. It is best suited for input-grounded tasks, like describing an image or speech recognition. You can also use `do_sample=True` with beam search to sample at each step, but beam search will still greedily prune out low probability sequences between steps.

> [!TIP]
> Check out the [beam search visualizer](https://huggingface.co/spaces/m-ric/beam_search_visualizer) to see how beam search works.

Enable beam search with the `num_beams` parameter (should be greater than 1 otherwise it's equivalent to greedy search).

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to(device)

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", dtype=torch.float16).to(device)
# explicitly set to 100 because Llama2 generation length is 4096
outputs = model.generate(**inputs, max_new_tokens=50, num_beams=2)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
"['Hugging Face is an open-source company that develops and maintains the Hugging Face platform, which is a collection of tools and libraries for building and deploying natural language processing (NLP) models. Hugging Face was founded in 2018 by Thomas Wolf']"
```

## Advanced decoding methods

Advanced decoding methods aim at either tackling specific generation quality issues (e.g. repetition) or at improving the generation throughput in certain situations. These techniques are more complex, and may not work correctly with all models.

### Speculative decoding

[Speculative](https://hf.co/papers/2211.17192) or assistive decoding isn't a search or sampling strategy. Instead, speculative decoding adds a second smaller model to generate candidate tokens. The main model verifies the candidate tokens in a single `forward` pass, which speeds up the decoding process overall. This method is especially useful for LLMs where it can be more costly and slower to generate tokens. Refer to the [speculative decoding](./llm_optims#speculative-decoding) guide to learn more.

Currently, only greedy search and multinomial sampling are supported with speculative decoding. Batched inputs aren't supported either.

Enable speculative decoding with the `assistant_model` parameter. You'll notice the fastest speed up with an assistant model that is much smaller than the main model. Add `do_sample=True` to enable token validation with resampling.

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-1.7B")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-1.7B")
assistant_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-135M")
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt")

outputs = model.generate(**inputs, assistant_model=assistant_model)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
'Hugging Face is an open-source company that provides a platform for developers to build and deploy machine'
```

Speculative decoding is also supported in [Pipeline](/docs/transformers/v4.57.3/en/main_classes/pipelines#transformers.Pipeline) with the `assistant_model` parameter.

```python
from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="meta-llama/Llama-3.1-8B",
    assistant_model="meta-llama/Llama-3.2-1B",
    dtype=torch.bfloat16
)
pipe_output = pipe("Once upon a time, ", max_new_tokens=50, do_sample=False)
pipe_output[0]["generated_text"]
```

Add the `temperature` parameter to control sampling randomness. For speculative decoding, a lower temperature may improve latency.

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-1.7B")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-1.7B")
assistant_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-135M")
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt")

outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
'Hugging Face is an open-source company that is dedicated to creating a better world through technology.'
```

#### Prompt lookup decoding

[Prompt lookup decoding](./llm_optims#prompt-lookup-decoding) is a variant of speculative decoding that uses overlapping n-grams as the candidate tokens. It works well for input-grounded tasks such as summarization. Refer to the [prompt lookup decoding](./llm_optims#prompt-lookup-decoding) guide to learn more.

Enable prompt lookup decoding with the `prompt_lookup_num_tokens` parameter.

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device

device = infer_device()

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-1.7B")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-1.7B", dtype=torch.float16).to(device)
assistant_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-135M", dtype=torch.float16).to(device)
inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to(device)

outputs = model.generate(**inputs, assistant_model=assistant_model, max_new_tokens=20, prompt_lookup_num_tokens=5)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
'Hugging Face is an open-source company that provides a platform for developers to build and deploy machine learning models. It offers a variety of tools'
```

### Self-speculative decoding

Early exiting uses the earlier hidden states from the language modeling head as inputs, effectively skipping layers to yield a lower quality output. The lower quality output is used as the assistant output and self-speculation is applied to fix the output using the remaining layers. The final generated result from this self-speculative method is the same (or has the same distribution) as the original models generation.

The assistant model is also part of the target model, so the caches and weights can be shared, resulting in lower memory requirements.

For a model trained with early exit, pass `assistant_early_exit` to [generate()](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationMixin.generate).

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

prompt = "Alice and Bob"
checkpoint = "facebook/layerskip-llama3.2-1B"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")

model = AutoModelForCausalLM.from_pretrained(checkpoint)
outputs = model.generate(**inputs, assistant_early_exit=4, do_sample=False, max_new_tokens=20)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
```

#### Universal assisted decoding

Universal assisted decoding (UAD) enables the main and assistant models to use different tokenizers. The main models input tokens are re-encoded into assistant model tokens. Candidate tokens are generated in the assistant encoding which are re-encoded into the main model candidate tokens. The candidate tokens are verified as explained in [speculative decoding](#speculative-decoding).

Re-encoding involves decoding token ids into text and encoding the text with a different tokenizer. To prevent tokenization discrepancies during re-encoding, UAD finds the longest common sub-sequence between the source and target encodings to ensure the new tokens include the correct prompt suffix.

Add the `tokenizer` and `assistant_tokenizer` parameters to [generate()](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationMixin.generate) to enable UAD.

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

prompt = "Alice and Bob"

assistant_tokenizer = AutoTokenizer.from_pretrained("double7/vicuna-68m")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
inputs = tokenizer(prompt, return_tensors="pt")

model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b")
assistant_model = AutoModelForCausalLM.from_pretrained("double7/vicuna-68m")
outputs = model.generate(**inputs, assistant_model=assistant_model, tokenizer=tokenizer, assistant_tokenizer=assistant_tokenizer)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a']
```

## Custom generation methods

Custom generation methods enable specialized behavior such as:

- have the model continue thinking if it is uncertain;
- roll back generation if the model gets stuck;
- handle special tokens with custom logic;
- use specialized KV caches;

We enable custom generation methods through model repositories, assuming a specific model tag and file structure (see subsection below). This feature is an extension of [custom modeling code](./models#custom-models) and, like such, requires setting `trust_remote_code=True`.

If a model repository holds a custom generation method, the easiest way to try it out is to load the model and generate with it:

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

# `transformers-community/custom_generate_example` holds a copy of `Qwen/Qwen2.5-0.5B-Instruct`, but
# with custom generation code -> calling `generate` uses the custom generation method!
tokenizer = AutoTokenizer.from_pretrained("transformers-community/custom_generate_example")
model = AutoModelForCausalLM.from_pretrained(
    "transformers-community/custom_generate_example", device_map="auto", trust_remote_code=True
)

inputs = tokenizer(["The quick brown"], return_tensors="pt").to(model.device)
# The custom generation method is a minimal greedy decoding implementation. It also prints a custom message at run time.
gen_out = model.generate(**inputs)
# you should now see its custom message, "✨ using a custom generation method ✨"
print(tokenizer.batch_decode(gen_out, skip_special_tokens=True))
'The quick brown fox jumps over a lazy dog, and the dog is a type of animal. Is'
```

Model repositories with custom generation methods have a special property: their generation method can be loaded from **any** model through [generate()](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationMixin.generate)'s `custom_generate` argument. This means anyone can create and share their custom generation method to potentially work with any Transformers model, without requiring users to install additional Python packages.

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", device_map="auto")

inputs = tokenizer(["The quick brown"], return_tensors="pt").to(model.device)
# `custom_generate` replaces the original `generate` by the custom generation method defined in
# `transformers-community/custom_generate_example`
gen_out = model.generate(**inputs, custom_generate="transformers-community/custom_generate_example", trust_remote_code=True)
print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
'The quick brown fox jumps over a lazy dog, and the dog is a type of animal. Is'
```

You should read the `README.md` file of the repository containing the custom generation strategy to see what the new arguments and output type differences are, if they exist. Otherwise, you can assume it works like the base [generate()](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationMixin.generate) method.

> [!TIP]
> You can find all custom generation methods by [searching for their custom tag.](https://huggingface.co/models?other=custom_generate), `custom_generate`.

Consider the Hub repository [transformers-community/custom_generate_example](https://huggingface.co/transformers-community/custom_generate_example) as an example. The `README.md` states that it has an additional input argument, `left_padding`, which adds a number of padding tokens before the prompt.

```py
gen_out = model.generate(
    **inputs, custom_generate="transformers-community/custom_generate_example", trust_remote_code=True, left_padding=5
)
print(tokenizer.batch_decode(gen_out)[0])
'The quick brown fox jumps over the lazy dog.\n\nThe sentence "The quick'
```

If the custom method has pinned Python requirements that your environment doesn't meet, you'll get an exception about missing requirements. For instance, [transformers-community/custom_generate_bad_requirements](https://huggingface.co/transformers-community/custom_generate_bad_requirements) has an impossible set of requirements defined in its `custom_generate/requirements.txt` file, and you'll see the error message below if you try to run it.

```text
ImportError: Missing requirements in your local environment for `transformers-community/custom_generate_bad_requirements`:
foo (installed: None)
bar==0.0.0 (installed: None)
torch>=99.0 (installed: 2.6.0)
```

Updating your Python requirements accordingly will remove this error message.

### Creating a custom generation method

To create a new generation method, you need to create a new [**Model**](https://huggingface.co/new) repository and push a few files into it.

1. The model you've designed your generation method with.
2. `custom_generate/generate.py`, which contains all the logic for your custom generation method.
3. `custom_generate/requirements.txt`, used to optionally add new Python requirements and/or lock specific versions to correctly use your method.
4. `README.md`, where you should add the `custom_generate` tag and document any new arguments or output type differences of your custom method here.

After you've added all required files, your repository should look like this

```text
your_repo/
├── README.md          # include the 'custom_generate' tag
├── config.json
├── ...
└── custom_generate/
    ├── generate.py
    └── requirements.txt
```

#### Adding the base model

The starting point for your custom generation method is a model repository just like any other. The model to add to this repository should be the model you've designed your method with, and it is meant to be part of a working self-contained model-generate pair. When the model in this repository is loaded, your custom generation method will override `generate`. Don't worry -- your generation method can still be loaded with any other Transformers model, as explained in the section above.

If you simply want to copy an existing model, you can do

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("source/model_repo")
model = AutoModelForCausalLM.from_pretrained("source/model_repo")
tokenizer.save_pretrained("your/generation_method", push_to_hub=True)
model.save_pretrained("your/generation_method", push_to_hub=True)
```

#### generate.py

This is the core of your generation method. It *must* contain a method named `generate`, and this method *must* contain a `model` argument as its first argument. `model` is the model instance, which means you have access to all attributes and methods in the model, including the ones defined in [GenerationMixin](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationMixin) (like the base `generate` method).

> [!WARNING]
> `generate.py` must be placed in a folder named `custom_generate`, and not at the root level of the repository. The file paths for this feature are hardcoded.

Under the hood, when the base [generate()](/docs/transformers/v4.57.3/en/main_classes/text_generation#transformers.GenerationMixin.generate) method is called with a `custom_generate` argument, it first checks its Python requirements (if any), then locates the custom `generate` method in `generate.py`, and finally calls the custom `generate`. All received arguments and `model` are forwarded to your custom `generate` method, with the exception of the arguments used to trigger the custom generation (`trust_remote_code` and `custom_generate`).

This means your `generate` can have a mix of original and custom arguments (as well as a different output type) as shown below.

```py
import torch

def generate(model, input_ids, generation_config=None, left_padding=None, **kwargs):
    generation_config = generation_config or model.generation_config  # default to the model generation config
    cur_length = input_ids.shape[1]
    max_length = generation_config.max_length or cur_length + generation_config.max_new_tokens

    # Example of custom argument: add `left_padding` (integer) pad tokens before the prompt
    if left_padding is not None:
        if not isinstance(left_padding, int) or left_padding  [!TIP]
> If you publish a `custom_generate` repository, your `generate` implementation can itself define a callable and pass it to `model.generate()`. This lets you customize the decoding loop while still benefiting from Transformers' built-in input preparation logic.

### Finding custom generation methods

You can find all custom generation methods by [searching for their custom tag.](https://huggingface.co/models?other=custom_generate), `custom_generate`. In addition to the tag, we curate two collections of `custom_generate` methods:

- [Custom generation methods - Community](https://huggingface.co/collections/transformers-community/custom-generation-methods-community-6888fb1da0efbc592d3a8ab6) -- a collection of powerful methods contributed by the community;
- [Custom generation methods - Tutorials](https://huggingface.co/collections/transformers-community/custom-generation-methods-tutorials-6823589657a94940ea02cfec) -- a collection of reference implementations for methods that previously were part of `transformers`, as well as tutorials for `custom_generate`.

## Resources

Read the [How to generate text: using different decoding methods for language generation with Transformers](https://huggingface.co/blog/how-to-generate) blog post for an explanation of how common decoding strategies work.