- The article explains how to use Llama-2, a generative AI model, to generate text and code without repeating the input prompt.
- The article shows how to load and use Llama-2 with the Hugging Face Transformers library, and how to modify the script to skip the special tokens that mark the input prompt.
- The article also provides some FAQs, tips, and examples on how to use Llama-2 effectively for various tasks and domains.
Llama-2 is a family of large language models (LLMs) that can generate text and code in response to natural language prompts. However, by default, Llama-2 also includes the input prompt in the output, which can be redundant and unnecessary. In this article, we will show you how to modify your script to get only the answer from Llama-2.
Table of Contents
What is Llama-2?
Llama-2 is a collection of pretrained and fine-tuned LLMs that range in size from 7 billion to 70 billion parameters. The pretrained models are trained on a large corpus of text and code from various sources, such as Wikipedia, GitHub, Stack Overflow, and Reddit. The fine-tuned models are optimized for dialogue applications using reinforcement learning from human feedback.
Llama-2 is based on the transformer architecture, which uses attention mechanisms to learn the relationships between words and tokens. Llama-2 also uses grouped-query attention, which reduces the computational complexity of the attention operation and enables fast inference of the 70 billion parameter model.
Llama-2 is released by Meta, a research organization dedicated to creating and publishing open and accessible AI systems. Llama-2 is available for commercial use under the Llama 2 license, which allows users to integrate the models into their products and services.
How to Use Llama-2?
Llama-2 can be used with the Hugging Face Transformers library, which provides a high-level API for working with various NLP models. To use Llama-2, you need to install the Transformers library and download the model and tokenizer from the Hugging Face Hub.
The model and tokenizer can be loaded using the AutoModelForCausalLM and AutoTokenizer classes, which automatically infer the model type and configuration from the model name. For example, to load the 7 billion parameter model with chat fine-tuning, you can use the following code:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
To generate text or code from Llama-2, you need to provide a prompt as an input. The prompt can be any natural language query or instruction that specifies what you want Llama-2 to generate. For example, you can ask Llama-2 to explain a concept, write a poem or code, solve a logic puzzle, or name your pet.
The prompt needs to be encoded into a sequence of tokens using the tokenizer. The tokenizer converts the prompt into a numerical representation that can be processed by the model. The encoded prompt is then passed as an argument to the generate method of the model, which returns a sequence of tokens as an output. The output tokens can be decoded back into text using the tokenizer.
For example, to generate a poem about love using Llama-2, you can use the following code:
prompt = "Write a poem about love"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
The output might look something like this:
Write a poem about love
Love is more than a feeling
It is a choice and a commitment
It is not always easy or smooth
But it is always worth it
Love is not selfish or greedy
It is generous and kind
It does not seek its own way
But considers the other's mind
Love is not proud or rude
It is humble and polite
It does not boast or envy
But rejoices in the truth and right
Love is not easily angered
It is patient and forgiving
It does not keep a record of wrongs
But bears all things and hopes all things
Love never fails or fades
It is faithful and enduring
It is stronger than death and time
It is eternal and secure
How to Get Only the Answer from Llama-2?
As you can see from the previous example, Llama-2 includes the input prompt in the output by default. This can be useful for some applications, such as dialogue systems or interactive learning platforms. However, for other applications, such as content creation or data analysis, you might want to get only the answer from Llama-2 without repeating the input prompt.
To achieve this, you need to modify your script slightly. The key idea is to use the skip_special_tokens argument of the decode method of the tokenizer. This argument allows you to remove any special tokens from the output sequence, such as <s> (start of sequence) and </s> (end of sequence). These tokens are used by Llama-2 to mark the boundaries of the input and output sequences, and they are usually not visible in the decoded text. However, by skipping them, you can also skip the input prompt, which is enclosed by these tokens.
To illustrate this, let’s use the same example as before, but this time, let’s add the skip_special_tokens argument to the decode method and set it to True. The modified code looks like this:
prompt = "Write a poem about love"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
The output now looks like this:
Love is more than a feeling
It is a choice and a commitment
It is not always easy or smooth
But it is always worth it
Love is not selfish or greedy
It is generous and kind
It does not seek its own way
But considers the other's mind
Love is not proud or rude
It is humble and polite
It does not boast or envy
But rejoices in the truth and right
Love is not easily angered
It is patient and forgiving
It does not keep a record of wrongs
But bears all things and hopes all things
Love never fails or fades
It is faithful and enduring
It is stronger than death and time
It is eternal and secure
As you can see, the output now contains only the answer from Llama-2, without repeating the input prompt. This can make the output more concise and clear, especially for longer or more complex prompts.
Frequently Asked Questions (FAQs)
Question: What are the advantages and disadvantages of using Llama-2?
Answer: Llama-2 has several advantages over other generative AI models, such as:
- It is available for commercial use under a permissive license.
- It has a large range of scales and domains to choose from.
- It has high performance on various natural language tasks.
- It has fine-tuned models for dialogue applications that are helpful and safe.
However, Llama-2 also has some disadvantages, such as:
- It requires a lot of computational resources to run and train.
- It can generate inaccurate or biased outputs that need human validation.
- It can be misused or abused for malicious purposes.
Question: How to fine-tune Llama-2 for custom tasks or domains?
Answer: Llama-2 can be fine-tuned for custom tasks or domains using the Hugging Face Transformers library. You need to prepare your own dataset of input-output pairs that match your task or domain. You can then use the Trainer class to fine-tune Llama-2 on your dataset using a suitable loss function and optimizer. You can also use the TrainingArguments class to customize various training parameters, such as learning rate, batch size, number of epochs, etc. For more details, you can refer to this tutorial on how to fine-tune Llama-2 for text generation.
Question: How to prompt Llama-2 effectively?
Answer: Prompting Llama-2 effectively means providing clear and specific instructions that guide Llama-2 to generate the desired output. Some general tips for prompting Llama-2 are:
- Use natural language that is easy to understand and follow.
- Provide enough context and information for Llama-2 to generate relevant and accurate outputs.
- Use keywords or phrases that indicate the type and format of the output you want.
- Use examples or templates to illustrate your expectations.
- Use feedback or constraints to correct or improve Llama-2’s outputs.
For more tips and examples on how to prompt Llama-2, you can refer to this guide on how to prompt large language models.
Summary
In this article, we have learned how to use Llama-2, a powerful generative AI model, to generate text and code without repeating the input prompt. We have also learned about the benefits and applications of Llama-2, as well as some FAQs related to Llama-2. We hope this article has helped you understand how to use Llama-2 effectively for your projects.
Disclaimer: This article is based on the information provided by Meta and Hugging Face. The author is not affiliated with Meta or Hugging Face. The opinions expressed in this article are solely those of the author and do not necessarily reflect those of Meta or Hugging Face. The author does not guarantee the accuracy or completeness of the information in this article. The author is not responsible for any errors or omissions in this article, or for any damages arising from its use.