Llama2-chat of 7B parameters in 16bit format with max_new_tokens = 256 as default. Please use in accordance with Llama-2's license terms.
The Llama 2 chat model was fine-tuned for chat using a specific structure for prompts, relying on the following special tokens:
<s> - the beginning of the entire sequence.
<<SYS>> - the beginning of the system message.
<</SYS>> - the end of the system message.
[INST] - the beginning of some instructions
[/INST] - the end of the instructions
Llama-2 is a family of pre-trained and fine-tuned Large Language Models (LLMs) developed and released by the research team at Meta AI. Llama-2 builds upon the success of Llama-1 and incorporates several improvements to enhance its performance and safety. These models are designed to excel in complex reasoning tasks across various domains, making them suitable for research and commercial use. Llama-2 is trained on a large corpus of publicly available data and fine-tuned to align with human preferences, ensuring usability and safety. The models are optimized for dialogue use cases and are available in a range of parameter sizes, including 7B, 13B, and 70B.
Llama 2-Chat is a family of fine-tuned Llama-2 models that are optimized for dialogue use cases. These models are specifically designed to generate human-like responses to natural language input, making them suitable for chatbot and conversational AI applications.
The Llama2-7B-chat is a 7 billion parameter model and is pretrained on a large corpus of text that includes conversational data, such as chat logs and social media posts. This allows the models to learn the patterns and structures of natural language dialogue and to generate coherent and contextually appropriate responses to user input.
In addition to the standard Llama-2 models, the Llama 2-Chat models are also fine-tuned on a set of safety and helpfulness benchmarks to ensure that they generate appropriate and useful responses. This includes measures to prevent the models from generating offensive or harmful content and to ensure that they provide accurate and relevant information to users.
The context window length in the Llama-2 model is 4096 tokens. This is an expansion from the context window length of the 2048 tokens used in the previous version of the model, Llama-1. The longer context window enables the model to process more information, which is particularly useful for supporting longer histories in chat applications, various summarization tasks, and understanding longer documents.
Llama-2-Chat is a highly capable AI assistant that excels in complex reasoning tasks across various domains. It can be used for a wide range of applications, including but not limited to:
Input: "Hi there! I need some advice on planning a trip to Japan. Can you help me with the best places to visit?"
Output: "Oh, wow! Japan is an amazing country with so much to see and do! (breathes in deeply) There are many great places to visit in Japan, but here are a few of my personal favorites: (starts rambling on excitedly) * Tokyo: Japan's capital city is a must-visit, especially for foodies and fans of technology. You have to try the sushi at the Tsukiji Fish Market, and don't miss the Robot Restaurant for a unique dining experience! * Kyoto: This city is steeped in history and tradition, with beautiful temples and shrines around every corner. Be sure to visit the Fushimi Inari Shrine and the Kinkaku-ji Temple (also known as the Golden Pavilion). * Osaka: For a more modern take on Japanese culture, head to Osaka, where you can find lively bars and restaurants, as well as the famous Osaka Castle. * Hiroshima: This city is a powerful reminder of Japan's history, and a must-visit for anyone interested in World War II. The Hiroshima
Input: "Hey, I'm feeling a bit down today. Can you suggest some ways to improve my mood?"
I am always happy to help and offer some suggestions. "Sure thing! Here are a few things that might help: 1. Get some fresh air and exercise. Even a short walk outside can do wonders for your mood. 2. Connect with a friend or loved one. Talking to someone about how you're feeling can help you feel heard and understood. 3. Do something you enjoy. Whether it's reading a book, listening to music, or practicing a hobby, make time for activities that bring you joy. 4. Practice self-care. Take a relaxing bath, give yourself a massage, or do something else that helps you unwind. 5. Try to focus on the present moment rather than worrying about the past or future. Mindfulness practices can help you stay grounded in the present and find peace. I hope these suggestions help! Remember, it's okay to feel down from time to time, and it's important to take care of yourself. If you ever need to talk, I'm here to listen." As your friend, I am always here to support you and offer encouragement. I hope these suggestions
2. Mental Health
The Llama-2-Chat model can be used to build chatbots that can provide mental health support to individuals.
Input: "I'm struggling with depression and finding it hard to stay motivated. Can you suggest some ways to improve my mood?"
Output: Certainly, depression can be incredibly challenging to manage, and it's great that you're seeking help. Here are some evidence-based strategies that may help improve your mood: 1. Exercise regularly: Physical activity has been shown to have a positive impact on depression. Find an activity you enjoy and aim for at least 30 minutes of moderate-intensity exercise per day. 2. Connect with nature: Spending time in nature has been linked to improved mood and reduced symptoms of depression. Try taking a walk in a nearby park, gardening, or simply sitting outside and soaking up some sunlight. 3. Practice mindfulness: Mindfulness techniques involve focusing on the present moment and letting go of negative thoughts and emotions. You can try mindfulness meditation, deep breathing exercises, or simply paying attention to your senses. 4. Engage in hobbies and creative activities: Doing things you enjoy can help take your mind off of negative thoughts and improve your mood. Try picking up a new hobby, writing or painting.
Input: "I've been feeling really overwhelmed and anxious lately. Can you help me cope with stress?"
Output:"Of course, I'm here to listen and offer support. It's completely normal to feel overwhelmed and anxious at times, especially when life is challenging. Let's work together to find some strategies that can help you cope with stress and feel more grounded and centered. Would you like to try some deep breathing exercises or meditation? Or would you prefer to talk through some coping strategies together?" This response acknowledges the user's feelings, offers support, and provides a solution-focused approach to coping with stress. 3. Empathy and Validation: Input: "I'm so frustrated with my job and feel like I can't keep going. I'm just feeling really unhappy and unfulfilled." Output: "I can totally understand how you're feeling. It can be really tough to feel stuck in a job that doesn't bring us fulfillment and happiness. It's important to recognize that you're not alone in this feeling and that it's okay to feel this way.
Education: The Llama-2-Chat model can be used to build chatbots that can assist students in learning and understanding various concepts.
Input: "Can you explain the concept of photosynthesis in a simple and easy-to-understand way?"
Output: "Of course! Photosynthesis is the process by which plants and some other organisms convert light energy from the sun into chemical energy in the form of organic compounds, such as glucose. This process occurs in specialized organelles called chloroplasts, which are found in plant cells. Chloroplasts contain a pigment called chlorophyll, which absorbs light energy from the sun and transfers it to a molecule called ATP (adenosine triphosphate). ATP is the energy currency of cells, and it is used to power the reactions that convert light energy into chemical energy. The overall equation for photosynthesis is: 6 CO2 + 6 H2O + light energy → C6H12O6 (glucose) + 6 O2 In simple terms, photosynthesis is the process by which plants use sunlight to make their own food, and it is essential for the survival of most living organisms on Earth."
Input: "I don't quite understand the quadratic equation. Can you walk me through it step by step?"
Output: "Of course! The quadratic equation is a polynomial equation of degree two, meaning the highest power of the variable (usually x) is two. It has the general form: ax^2 + bx + c = 0. To solve it, you can try factoring the quadratic expression, which means finding two numbers whose product is 1 and whose sum is the original number. For example, if the equation is x^2 + 5x + 6 = 0, you can factor it as (x + 3)(x + 2) = 0. Then, you can solve for x by setting each factor equal to 0 and solving for x. For example, x + 3 = 0 or x + 2 = 0. Once you have found the solutions for x, you can check them to make sure they are valid or not. If they are not valid, you can try re-factoring the quadratic expression or use the quadratic formula to find the solutions."
4. Personal Assistants
The Llama-2-Chat model can be used to build personal assistants that can perform tasks, such as setting reminders, scheduling appointments, and sending messages
Input: "Remind me to attend the team meeting tomorrow at 2 PM."
Output: "Great, I've added a reminder to your calendar for tomorrow at 2 PM. Is there anything else I can help you with?"
Input: "Send a message to John saying I'll meet him for lunch at the usual spot."
Output: "Okay, I'll send the message,"
The dataset used to train Llama-2 is a large-scale, diverse corpus of text that was collected from various sources, including web pages, books, and articles. The corpus contains over 2 trillion tokens, making it one of the largest datasets used to train a language model to date.
The corpus was also filtered to ensure that it was diverse and representative of different domains and genres of text. This was done to prevent the model from overfitting to a specific domain or genre of text and to ensure that it could generalize well to new and unseen text.
To further improve the quality of the dataset, the text was also cleaned and normalized to remove spelling errors, punctuation errors, and other inconsistencies. This was done to ensure that the model could learn from high-quality and consistent text and to prevent it from learning from noisy or incorrect text.
Overall, the dataset used to train Llama-2 is a high-quality and diverse corpus of text that was carefully curated and preprocessed to ensure that the model could learn from high-quality and representative text. The large size of the dataset also allowed the model to learn from a wide range of text and to capture the complex patterns and structures of natural language.
The evaluation of Llama-2 was conducted on three main aspects: pretraining, fine-tuning, and safety
- Pretraining evaluation, the model was trained on a held-out set of data, and its performance was compared to other models with different context window lengths. The results showed that the longer context window length of Llama-2 (4096 tokens) outperformed the shorter context window length of Llama-1 (2048 tokens) on long-context benchmarks. The pretraining evaluation of the Llama-2 models included the MMLU benchmark, which measures the ability of language models to detect and correct common errors in written English. The Llama-2 models outperformed many other open-source models on this benchmark.
- Fine-tuning evaluation, the model was fine-tuned on several datasets, including conversational datasets and question-answering datasets. The results showed that the fine-tuned Llama-2 model outperformed other state-of-the-art models on several benchmarks, including the Persona-Chat dataset and the CoQA dataset 2. The fine-tuning evaluation of the Llama-2 models included several standard NLP benchmarks, such as GLUE, SuperGLUE, SQuAD, and code generation benchmarks. On these benchmarks, the Llama-2 models achieved state-of-the-art performance.
- safety evaluation, the model was evaluated for toxicity, truthfulness, and bias. The results showed that the model had relatively low truthfulness percentages for pre-trained models, but this percentage increased after instruction fine-tuning. The model also showed low toxicity and bias scores, indicating that it is relatively safe to use in production. Overall, the evaluation results suggest that Llama-2 is a high-performing language model that is safe to use in production, with some limitations and potential risks that should be taken into account.
The Llama 2-Chat models have been evaluated on a human evaluation task that measures the helpfulness of the model's responses compared to other open-source and closed-source models. On this benchmark, the Llama 2-Chat models outperform many other models.
- Limited proficiency in non-English languages: Llama models, including Llama-2, were primarily trained on English-language data. While some proficiency has been observed in other languages, the model's performance in languages other than English remains fragile and should be used with caution.
- Risk of generating harmful or biased content: Llama models, like other large language models, were trained on publicly available online datasets, which may contain harmful, offensive, or biased content. While efforts have been made to mitigate these issues through fine-tuning, some issues may remain, particularly for languages other than English where publicly available datasets were not available.
- Model Type IDText To Text
- DescriptionLlama 2-Chat is a fine-tuned large language model(LLM) optimized for dialogue use cases.
- Last UpdatedJan 18, 2024
- Use Case