library_name: keras-hub
Model Overview
Phi-3 is a set of large language models published by Microsoft. Models are instruction tuned, and range in size from 3 billion to 14 billion parameters. See the model card below for benchmarks, data sources, and intended use cases.
Weights are released under the MIT License. Keras model code is released under the Apache 2 License.
Links
- Phi-3 Quickstart Notebook
- Phi-3 API Documentation
- Phi-3 Model Card
- KerasHub Beginner Guide
- KerasHub Model Publishing Guide
Installation
Keras and KerasHub can be installed with:
pip install -U -q keras-hub
pip install -U -q keras>=3
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the Keras Getting Started page.
Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|---|---|---|
phi3_mini_4k_instruct_en |
3.82B | 3B model with 4K max context |
phi3_mini_128k_instruct_en |
3.82B | 3B model with 128K max context |
Prompts
Phi-3 models are instruction tuned on turn by turn conversations and should be prompted with examples that precisely match the training data. Specifically, you must alternate user and assistant turns that begin and end with special tokens. New lines do matter. See the following for an example:
prompt = """<|user|>
Hello!<|end|>
<|assistant|>
Hello! How are you?<|end|>
<|user|>
I'm great. Could you help me with a task?<|end|>
"""
Example Usage
pip install -U -q keras-hub
import keras
import keras_hub
import numpy as np
Use generate() to do text generation.
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset("phi3_mini_128k_instruct_en")
phi3_lm.generate("<|user|>\nHow to explain Internet for a medieval knight?<|end|>\n<|assistant|>", max_length=500)
# Generate with batched prompts.
phi3_lm.generate([
"<|user|>\nWhat is Keras?<|end|>\n<|assistant|>",
"<|user|>\nGive me your best brownie recipe.<|end|>\n<|assistant|>",
], max_length=500)
Compile the generate() function with a custom sampler.
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset("phi3_mini_128k_instruct_en")
phi3_lm.compile(sampler="greedy")
phi3_lm.generate("<|user|>\nWhat is Keras?<|end|>\n<|assistant|>", max_length=30)
phi3_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
phi3_lm.generate("<|user|>\nWhat is Keras?<|end|>\n<|assistant|>", max_length=30)
Use generate() without preprocessing.
prompt = {
"token_ids": np.array([[306, 864, 304, 1827, 0, 0, 0, 0, 0, 0]] * 2),
# Use `"padding_mask"` to indicate values that should not be overridden.
"padding_mask": np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0]] * 2),
}
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset(
"phi3_mini_128k_instruct_en",
preprocessor=None,
dtype="bfloat16"
)
phi3_lm.generate(prompt)
Call fit() on a single batch.
features = ["The quick brown fox jumped.", "I forgot my homework."]
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset("phi3_mini_128k_instruct_en")
phi3_lm.fit(x=features, batch_size=2)
Example Usage with Hugging Face URI
pip install -U -q keras-hub
import keras
import keras_hub
import numpy as np
Use generate() to do text generation.
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset("hf://keras/phi3_mini_128k_instruct_en")
phi3_lm.generate("<|user|>\nHow to explain Internet for a medieval knight?<|end|>\n<|assistant|>", max_length=500)
# Generate with batched prompts.
phi3_lm.generate([
"<|user|>\nWhat is Keras?<|end|>\n<|assistant|>",
"<|user|>\nGive me your best brownie recipe.<|end|>\n<|assistant|>",
], max_length=500)
Compile the generate() function with a custom sampler.
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset("hf://keras/phi3_mini_128k_instruct_en")
phi3_lm.compile(sampler="greedy")
phi3_lm.generate("<|user|>\nWhat is Keras?<|end|>\n<|assistant|>", max_length=30)
phi3_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
phi3_lm.generate("<|user|>\nWhat is Keras?<|end|>\n<|assistant|>", max_length=30)
Use generate() without preprocessing.
prompt = {
"token_ids": np.array([[306, 864, 304, 1827, 0, 0, 0, 0, 0, 0]] * 2),
# Use `"padding_mask"` to indicate values that should not be overridden.
"padding_mask": np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0]] * 2),
}
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset(
"hf://keras/phi3_mini_128k_instruct_en",
preprocessor=None,
dtype="bfloat16"
)
phi3_lm.generate(prompt)
Call fit() on a single batch.
features = ["The quick brown fox jumped.", "I forgot my homework."]
phi3_lm = keras_hub.models.Phi3CausalLM.from_preset("hf://keras/phi3_mini_128k_instruct_en")
phi3_lm.fit(x=features, batch_size=2)