How one can Entry Phi-4 Utilizing Hugging Face?

Microsoft’s Phi-4 mannequin is out there on Hugging Face, providing builders a robust instrument for superior textual content technology and reasoning duties. On this article, we’ll stroll you thru the steps to entry and use Phi-4, from making a Hugging Face account to producing outputs with the mannequin. We’ll additionally discover key options, together with its optimized efficiency for reminiscence and compute-constrained environments, and how one can successfully use Phi-4 in numerous purposes.

Phi 4 and its Options

Phi-4,  is a state-of-the-art language mannequin designed for superior reasoning and high-quality textual content technology. On this Phi-4, we’re having about 14 billion parameters that align properly in reminiscence and computationally restricted eventualities to make it extremely appropriate for builders looking for to include environment friendly synthetic intelligence of their purposes.

The Phi-4 mannequin follows a decoder-only transformer structure with 14 billion parameters, designed to course of textual content by means of a classy pipeline. At its core, the enter textual content is first tokenized utilizing the Tiktoken tokenizer with a vocabulary measurement of 100,352, which then feeds into the token embedding layer. The primary transformer structure consists of a number of layers of self-attention mechanisms able to dealing with a 16K token context window (expanded from 4K throughout midtraining), adopted by feed-forward networks.

The mannequin was educated on roughly 10 trillion tokens with a various knowledge composition: 40% artificial knowledge, 15% internet rewrites, 15% filtered internet knowledge, 20% code knowledge, and 10% focused acquisitions. The coaching pipeline progressed by means of three primary phases: pre-training (with 4K context), mid-training (expanded to 16K context), and fine-tuning. Put up-training enhancements included Supervised Wonderful-tuning (SFT), Direct Choice Optimization (DPO) with pivotal token search, and judge-guided knowledge, culminating in a language mannequin that outputs likelihood distributions over its vocabulary to generate responses.

You may learn extra about Phi-4 right here.

Options of Phi-4

• Context Size: Phi-4 helps a context size of as much as 16,000 tokens, permitting for intensive conversations or detailed textual content technology.
• Security Measures: The mannequin incorporates strong security options, together with supervised fine-tuning and choice optimization, to make sure secure and useful interactions.

Stipulations

Earlier than getting began into the method of accessing PHI 4, be sure you have the next stipulations:

• Hugging Face Account: You will have a Hugging Face account to entry and use fashions from the Hub.
• Python Surroundings: Guarantee you’ve Python 3.7 or later put in in your machine.
• Libraries: Set up the required libraries.

Use the next instructions to put in them:

pip set up transformers
pip set up torch

How one can Entry Phi-4 Utilizing Hugging Face?

Beneath we’ll present you how you can simply entry and make the most of Microsoft’s Phi-4 mannequin on Hugging Face, enabling highly effective textual content technology and reasoning capabilities to your purposes. Observe our step-by-step directions to get began rapidly and effectively.

Step 1: Making a Hugging Face Account

To entry PHI 4 and different fashions, you first have to create an account on Hugging Face. Go to Hugging Face’s web site and join. After creating an account, you’ll be capable of entry personal and public fashions hosted on the platform.

Step 2: Authenticate with Hugging Face

To entry personal fashions like PHI 4, it’s good to authenticate your Hugging Face account. You should utilize the Hugging Face CLI instrument to take action:

Set up the CLI instrument:

pip set up huggingface_hub

Log in to your Hugging Face account by working the next command:

huggingface-cli login

Enter your credentials or token when prompted.

Step 3: Set up Required Libraries

First, guarantee you’ve the transformers library put in. You may set up it utilizing pip:

pip set up transformers

Step 4: Load the Phi-4 Mannequin

As soon as the library is put in, you may load the Phi-4 mannequin utilizing the pipeline API from Hugging Face. Right here’s how you are able to do it:

import transformers

# Load the Phi-4 mannequin
pipeline = transformers.pipeline(
    "text-generation",
    mannequin="microsoft/phi-4",
    model_kwargs={"torch_dtype": "auto"},
    device_map="auto",
)

Step 5: Put together Your Enter

Phi-4 is optimized for chat-style prompts. You may construction your enter as follows:

messages = [
    {"role": "system", "content": "You are a data scientist providing insights and explanations to a curious audience."},
    {"role": "user", "content": "How should I explain machine learning to someone new to the field?"},
]

Step 6: Producing Output

Use the pipeline to generate responses based mostly in your enter:

outputs = pipeline(messages, max_new_tokens=128)
print(outputs[0]['generated_text'])

Output:

Conclusion

Phi-4 is now absolutely accessible on Hugging Face, making it simpler than ever for builders and researchers to leverage its capabilities for numerous purposes. Whether or not you’re constructing chatbots, academic instruments, or any utility requiring superior language understanding, Phi-4 stands out as a robust possibility.

For additional particulars and updates, you may seek advice from the official Hugging Face documentation and discover the capabilities of this modern mannequin.

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