RWKV7CausalLM model

[source]

RWKV7CausalLM class

keras_hub.models.RWKV7CausalLM(backbone, preprocessor=None, **kwargs)

An end-to-end RWKV-7 model for causal language modeling.

A causal language model (LM) predicts the next token based on previous
tokens. This task setup can be used to train the model unsupervised on
plain text input, or to autoregressively generate plain text similar to
the data used for training. This task can be used for pre-training or
fine-tuning a RWKV-7 model, simply by calling `fit()`.

This model has a generate() method, which generates text based on a
prompt. The generation strategy used is controlled by an additional
sampler argument on `compile()`. You can recompile the model with
different `keras_hub.samplers` objects to control the generation. By
default, `"greedy"` sampling will be used.

# Arguments
    backbone: A [`keras_hub.models.RWKV7Backbone`](/keras_hub/api/models/rwkv7/rwkv7_backbone#rwkv7backbone-class) instance.
    preprocessor: A [`keras_hub.models.RWKV7CausalLMPreprocessor`](/keras_hub/api/models/rwkv7/rwkv7_causal_lm_preprocessor#rwkv7causallmpreprocessor-class) or `None`.
        If `None`, this model will not apply preprocessing, and inputs
        should be preprocessed before calling the model.

# Examples

```python
# Initialize the tokenizer and load assets from a local path.
tokenizer = RWKVTokenizer()
tokenizer.load_assets(rwkv_path)

# Create a preprocessor with a sequence length of 8.
preprocessor = RWKV7CausalLMPreprocessor(tokenizer, sequence_length=8)

# Initialize the model with a backbone and preprocessor.
causal_lm = RWKV7CausalLM(backbone, preprocessor)

# you also can load model by from_preset
rwkv_path = "RWKV7_G1a_0.1B"
tokenizer = RWKVTokenizer.from_preset(rwkv_path)
causal_lm = RWKV7CausalLM.from_preset(rwkv_path)

prompts = ["Bubble sort

```python", "Hello World

"]

    causal_lm.compile(sampler="greedy")

    outputs = causal_lm.generate(prompts, max_length=128)
    for out in outputs:
        print(out)
        print("-" * 100)
    ```



----

<span style="float:right;">[[source]](https://github.com/keras-team/keras-hub/tree/v0.26.0/keras_hub/src/models/task.py#L129)</span>

### `from_preset` method


```python
RWKV7CausalLM.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Task from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

For any Task subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

This constructor can be called in one of two ways. Either from a task specific base class like keras_hub.models.CausalLM.from_preset(), or from a model class like keras_hub.models.BertTextClassifier.from_preset(). If calling from the a base class, the subclass of the returning object will be inferred from the config in the preset directory.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, saved weights will be loaded into the model architecture. If False, all weights will be randomly initialized.

Examples

# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
    "gemma_2b_en",
)

# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
    "bert_base_en",
    num_classes=2,
)

[source]

generate method

RWKV7CausalLM.generate(
    inputs, max_length=None, stop_token_ids="auto", strip_prompt=False
)

Generate text given prompt inputs.

This method generates text based on given inputs. The sampling method used for generation can be set via the compile() method.

If inputs are a tf.data.Dataset, outputs will be generated "batch-by-batch" and concatenated. Otherwise, all inputs will be handled as a single batch.

If a preprocessor is attached to the model, inputs will be preprocessed inside the generate() function and should match the structure expected by the preprocessor layer (usually raw strings). If a preprocessor is not attached, inputs should match the structure expected by the backbone. See the example usage above for a demonstration of each.

Arguments

• inputs: python data, tensor data, or a tf.data.Dataset. If a preprocessor is attached to the model, inputs should match the structure expected by the preprocessor layer. If a preprocessor is not attached, inputs should match the structure expected the backbone model.
• max_length: Optional. int. The max length of the generated sequence. Will default to the max configured sequence_length of the preprocessor. If preprocessor is None, inputs should be should be padded to the desired maximum length and this argument will be ignored.
• stop_token_ids: Optional. None, "auto", or tuple of token ids. Defaults to "auto" which uses the preprocessor.tokenizer.end_token_id. Not specifying a processor will produce an error. None stops generation after generating max_length tokens. You may also specify a list of token id's the model should stop on. Note that sequences of tokens will each be interpreted as a stop token, multi-token stop sequences are not supported.
• strip_prompt: Optional. By default, generate() returns the full prompt followed by its completion generated by the model. If this option is set to True, only the newly generated text is returned.

backbone property

keras_hub.models.RWKV7CausalLM.backbone

A keras_hub.models.Backbone model with the core architecture.

preprocessor property

keras_hub.models.RWKV7CausalLM.preprocessor

A keras_hub.models.Preprocessor layer used to preprocess input.