Llama 2 is the next generation of our open-source large language model. It is available for free for research and commercial use.
Inside the Llama 2 model, you’ll find pretrained and fine-tuned language models like Llama Chat and Code Llama. These models range from 7B to 70B parameters and have been trained on 2 trillion tokens. The fine-tuned models have even been trained on over 1 million human annotations. So you can trust that they’re top-notch!
Regarding benchmarks, Llama 2 outperforms other open-source language models, including Llama 1, on reasoning, coding, proficiency, and knowledge tests. It’s really impressive!
Let’s dive into the details of Llama Chat. This model was pretrained on publicly available online data sources. The fine-tuned model, Llama Chat, leverages publicly available instruction datasets and over 1 million human annotations. So you can expect it to be super helpful in generating chat responses.
Llama 2 is a collection of pretrained and fine-tuned generative text models, ranging from 7 billion to 70 billion parameters. The fine-tuned LLMs, known as Llama-2-Chat, are specifically optimized for dialogue use cases. In fact, they outperform open-source chat models on most benchmarks. In terms of helpfulness and safety, the Llama-2-Chat models are on par with popular closed-source models like ChatGPT and PaLM.
🔗 Recommended: Llama 2: How Meta’s Free Open-Source LLM Beats GPT-4!
Llama vs Llama-2 – Key Differences and Similarities
The following table showcases the differences and similarities between Llama-1 and Llama-2 (source):
|Parameters||65B||70B, 13B, 7B|
|Training Data||1.56T tokens||2.2T tokens|
|Context Length||2048 tokens||4096 tokens|
|Attention Mechanism||Transformer||Grouped-query attention|
|Fine-tuned Models||❎ No||Yes (Llama 2-Chat)|
|Performance||Good||Better than Llama-1 on most benchmarks|
|Computing Overhead||High||Very high (70B model)|
|Open-Source?||✅ Yes||✅ Yes|
|RLHF?||❎ No||✅ Yes|
|Use Cases||Answering questions, generating text, translating languages||Llama-1 + reasoning, coding, proficiency tests|
Training Data and Context Length: Llama 2 models are trained on 40% more data than Llama and have double the context length. This means that Llama 2 has a better understanding of language and can provide more accurate and helpful responses to user queries.
Performance on External Benchmarks: Llama 2 has outperformed Llama on reasoning, coding, proficiency, and knowledge tests. This demonstrates its ability to excel in diverse tasks and provide users with more sophisticated language-based research, high-quality content generation, and accurate information.
Reinforcement Learning from Human Feedback (RLHF): Llama-2-chat, the fine-tuned version of Llama 2, uses reinforcement learning from human feedback during its training process. This makes the model safer and more helpful in conversations, addressing concerns related to responsible AI practices.
Privacy and Offline Accessibility: Both Llama and Llama 2 can be operated independently on local systems, making them suitable for applications where privacy or limited internet access is a concern. This feature ensures data security and enables offline use cases within a controlled environment.
Model Sizes: Llama is available in several sizes (7B, 13B, 33B, and 65B parameters) while Llama 2 is available in (7B, 13B, and 70B parameters).
- Availability: Both Llama-1 and Llama-2 are open-source.
- Languages Supported: They both support 20 languages.
- Number of Parameters: Llama-2 has models with different parameter sizes (70B, 13B, 7B), while Llama-1 has 65B.
- Training Data & Context Length: Llama-2 has been trained on more data and supports a longer context length.
- Attention Mechanism: They utilize different attention mechanisms.
- Fine-tuned Models: Llama-2 has a fine-tuned model, Llama 2-Chat, while Llama-1 does not.
- Performance: Llama-2 outperforms Llama-1 on most benchmarks.
- Computational Requirements: Llama-2, especially the 70B model, requires significantly more computational resources.
- Reinforcement Learning: Llama-2 incorporates reinforcement learning from human feedback, unlike Llama-1.
- Suitability: Llama-2 is better suited for more demanding tasks.
Programming is an area where AI advancements have already made a significant impact.
For example, GitHub launched Copilot, a coding plug-in that auto-completes code sections based on user input. Copilot uses OpenAI’s GPT model and has been well-received by developers, with over a million users and 200,000 businesses using it. Meta’s Code Llama is expected to offer similar benefits, with two versions tailored for Python code and natural language commands.
Meta has trained Code Llama on publicly available code, ensuring its accessibility. They offer three different model sizes, with the smallest one capable of running on a single GPU. This release opens up exciting possibilities for developers to leverage AI in their coding endeavors.
Amjad Masad, the CEO of Replit, an online coding platform, doesn’t think Code Llama will replace Copilot because it has limited training data. But he does think it’s exciting because developers can experiment with agents that can do useful tasks like browsing the web or booking a flight.
Meta, on the other hand, doesn’t have ChatGPT or an AI-powered search engine, but releasing Code Llama could give them an advantage in the race to harness generative AI. They decided to go with an open approach after someone leaked an early version of Llama to the web.
🚀 License: Both Llama 2 and Code Llama are not released under regular open-source licenses. Meta’s license restricts users from using the models in apps or services with more than 700 million monthly users.
Accessing Llama 2 on Hugging Face
To access Llama 2 on Hugging Face, you’ll first need to visit the Meta website and accept their license terms and acceptable use policy.
Once you’ve done that, you can fill out a form on Hugging Face to request access. Just make sure that the email address you provide matches the one you used on the Meta website. It usually takes about 1-2 days for your request to be processed. If you already have a Hugging Face account, you can log in or sign up to review the conditions and access the model content.
Llama 2 Model Details
Llama 2 is a collection of pretrained and fine-tuned generative text models. These models range in scale from 7 billion to 70 billion parameters. The specific model we’re talking about here is the 70B fine-tuned model, which is optimized for dialogue use cases. It has been converted for the Hugging Face Transformers format.
The Llama-2-Chat models and are designed for dialogue use cases. In fact, they perform better than open-source chat models on most benchmarks and are on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety.
Variations: Llama 2 comes in different parameter sizes, including 7B, 13B, and 70B. There are also pretrained and fine-tuned variations available.
Model Architecture: Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The fine-tuned versions of the model have been trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Training Data: The Llama 2 models have been trained on a mix of publicly available online data. The specific details vary depending on the parameter size of the model.
Intended Use: Llama 2 is intended for commercial and research use in English. The tuned models are specifically designed for assistant-like chat, while the pretrained models can be adapted for various natural language generation tasks.
Out-of-scope Uses: Using Llama 2 in any way that violates applicable laws or regulations, including trade compliance laws, is strictly prohibited. Additionally, using the models in languages other than English or in any other way that goes against the Acceptable Use Policy and Licensing Agreement for Llama 2 is not allowed.
Data Freshness: The pretraining data goes up until September 2022, but some of the tuning data is more recent, up to July 2023.
🔗 Recommended: Llama 2: How Meta’s Free Open-Source LLM Beats GPT-4!
Now here’s an encouraging thought from the paper: Even with the large Llama-2 models with 70B parameters, there’s still no saturation in performance!!! 🤯 We are just at the beginning of the AI revolution:
If you want to become a proficient Llama-2 prompt engineer, feel free to check out our academy course here:
Prompt Engineering with Llama 2
💡 The Llama 2 Prompt Engineering course helps you stay on the right side of change. Our course is meticulously designed to provide you with hands-on experience through genuine projects.
You’ll delve into practical applications such as book PDF querying, payroll auditing, and hotel review analytics. These aren’t just theoretical exercises; they’re real-world challenges that businesses face daily.
By studying these projects, you’ll gain a deeper comprehension of how to harness the power of Llama 2 using 🐍 Python, 🔗🦜 Langchain, 🌲 Pinecone, and a whole stack of highly ⚒️🛠️ practical tools of exponential coders in a post-ChatGPT world.
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
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His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.