README.md

metadata

library_name: keras-hub
license: apache-2.0
language:
  - en
tags:
  - text-generation
  - keras
pipeline_tag: text-generation

Model Overview

An OPT decoder network.

This class implements a Transformer-based decoder model as described in "OPT: Open Pre-trained Transformer Language Models". The default constructor gives a fully customizable, randomly initialized OPT model with any number of layers, heads, and embedding dimensions. To load preset architectures and weights, use the from_preset() constructor.

Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The underlying model is provided by a third party and subject to a separate license, available here.

Arguments

• vocabulary_size: int. The size of the token vocabulary.
• num_layers: int. The number of transformer decoder layers.
• num_heads: int. The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads.
• hidden_dim: int. The hidden size of the transformer decoder layers.
• intermediate_dim: int. The output dimension of the first Dense layer in a two-layer feedforward network for each transformer decoder layer.
• dropout: float. Dropout probability for the Transformer decoder.
• max_sequence_length: int. The maximum sequence length that this decoder can consume. If None, max_sequence_length uses the value from sequence length. This determines the variable shape for positional embeddings.

Example Usage

import keras
import keras_hub
import numpy as np

Use generate() to do text generation.

opt_lm = keras_hub.models.OPTCausalLM.from_preset("opt_2.7b_en")
opt_lm.generate("I want to say", max_length=30)

# Generate with batched prompts.
opt_lm.generate(["This is a", "Where are you"], max_length=30)

Compile the generate() function with a custom sampler.

opt_lm = keras_hub.models.OPTCausalLM.from_preset("opt_2.7b_en")
opt_lm.compile(sampler="greedy")
opt_lm.generate("I want to say", max_length=30)

opt_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
opt_lm.generate("I want to say", max_length=30)

Use generate() without preprocessing.

# Prompt the model with `5338, 318` (the token ids for `"Who is"`).
# Use `"padding_mask"` to indicate values that should not be overridden.
prompt = {
    "token_ids": np.array([[5338, 318, 0, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 0, 0, 0]] * 2),
}

opt_lm = keras_hub.models.OPTCausalLM.from_preset(
    "opt_2.7b_en",
    preprocessor=None,
)
opt_lm.generate(prompt)

Call fit() on a single batch.

features = ["The quick brown fox jumped.", "I forgot my homework."]
opt_lm = keras_hub.models.OPTCausalLM.from_preset("opt_2.7b_en")
opt_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

x = {
    "token_ids": np.array([[1, 2, 3, 4, 5]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[2, 3, 4, 5, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1]] * 2)

opt_lm = keras_hub.models.OPTCausalLM.from_preset(
    "opt_2.7b_en",
    preprocessor=None,
)
opt_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

Example Usage with Hugging Face URI

import keras
import keras_hub
import numpy as np

Use generate() to do text generation.

opt_lm = keras_hub.models.OPTCausalLM.from_preset("hf://keras/opt_2.7b_en")
opt_lm.generate("I want to say", max_length=30)

# Generate with batched prompts.
opt_lm.generate(["This is a", "Where are you"], max_length=30)

Compile the generate() function with a custom sampler.

opt_lm = keras_hub.models.OPTCausalLM.from_preset("hf://keras/opt_2.7b_en")
opt_lm.compile(sampler="greedy")
opt_lm.generate("I want to say", max_length=30)

opt_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
opt_lm.generate("I want to say", max_length=30)

Use generate() without preprocessing.

# Prompt the model with `5338, 318` (the token ids for `"Who is"`).
# Use `"padding_mask"` to indicate values that should not be overridden.
prompt = {
    "token_ids": np.array([[5338, 318, 0, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 0, 0, 0]] * 2),
}

opt_lm = keras_hub.models.OPTCausalLM.from_preset(
    "hf://keras/opt_2.7b_en",
    preprocessor=None,
)
opt_lm.generate(prompt)

Call fit() on a single batch.

features = ["The quick brown fox jumped.", "I forgot my homework."]
opt_lm = keras_hub.models.OPTCausalLM.from_preset("hf://keras/opt_2.7b_en")
opt_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

x = {
    "token_ids": np.array([[1, 2, 3, 4, 5]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[2, 3, 4, 5, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1]] * 2)

opt_lm = keras_hub.models.OPTCausalLM.from_preset(
    "hf://keras/opt_2.7b_en",
    preprocessor=None,
)
opt_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)