Llama-3.2-1B
Maintainer: meta-llama
274
📉
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The Llama-3.2-1B is part of the Meta Llama 3.2 collection of multilingual large language models (LLMs). This 1 billion parameter model is a pretrained and instruction-tuned generative model optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. It outperforms many available open source and closed chat models on common industry benchmarks, according to the maintainer meta-llama. Similar models in the Llama 3.2 family include the Llama-3.2-3B-Instruct and Llama-3.2-1B-Instruct.
Model inputs and outputs
The Llama-3.2-1B model takes multilingual text as input and generates multilingual text and code as output. It has a context length of 128k tokens and uses Grouped-Query Attention (GQA) for improved inference scalability.
Inputs
- Multilingual Text: The model supports input in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Outputs
- Multilingual Text: The model can generate text outputs in the same set of supported languages.
- Multilingual Code: In addition to text, the model can also generate code outputs in the supported languages.
Capabilities
The Llama-3.2-1B model is capable of a variety of natural language processing tasks, such as dialogue, summarization, and knowledge retrieval. Its multilingual capabilities allow it to be used in global applications beyond just English.
What can I use it for?
The Llama-3.2-1B model is intended for commercial and research use cases that require multilingual text generation and understanding. This could include building AI assistants for customer service, creating multilingual writing aids, or powering knowledge retrieval systems. Developers can also fine-tune the model further for their specific needs.
Things to try
One interesting thing to try with the Llama-3.2-1B model is its ability to generate text in multiple languages. You could experiment with prompts that require the model to understand context and switch between supported languages seamlessly. Additionally, the model's capabilities around code generation could be explored, such as using it to assist with programming tasks in different languages.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🐍
Llama-3.2-3B
128
The Llama-3.2-3B is part of the Meta Llama 3.2 collection of multilingual large language models (LLMs). This 3B parameter model is a pretrained and instruction-tuned generative model that supports text input and output in multiple languages. It is designed to excel at multilingual dialogue use cases, including tasks like knowledge retrieval and summarization. The Llama-3.2-1B is a smaller 1B parameter version of the same model architecture. Both the Llama-3.2-3B and Llama-3.2-1B models use an optimized transformer architecture and were trained using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align them with human preferences for helpfulness and safety. They were pretrained on up to 9 trillion tokens of publicly available data and fine-tuned for a variety of language tasks. Model inputs and outputs Inputs Multilingual Text**: The models support text input in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. They have also been trained on a broader set of languages beyond these 8 supported languages. Outputs Multilingual Text**: The models can generate text outputs in the same 8 supported languages as the inputs. Multilingual Code**: In addition to text, the models can also generate code in various programming languages. Capabilities The Llama-3.2-3B and Llama-3.2-1B models excel at a variety of language tasks, including dialogue, knowledge retrieval, summarization, and more. They outperform many open-source and commercial chatbots on common industry benchmarks. The models also have the ability to understand and generate code, making them useful for programming-related applications. What can I use it for? The Llama-3.2-3B and Llama-3.2-1B models are intended for commercial and research use in multiple languages. Potential use cases include: Multilingual assistant applications for tasks like customer support, query answering, and task completion AI-powered writing assistants that can help with editing, rewriting, and ideation Summarization and information retrieval tools that can work across languages Chatbots and dialogue systems that need to understand and respond in multiple languages The Llama 3.2 Community License governs the use of these models. Things to try One interesting aspect of the Llama-3.2-3B and Llama-3.2-1B models is their ability to handle long-form, multi-turn dialogue. You could try using them to build interactive chatbots or virtual assistants that can engage in extended conversations across multiple languages. Another interesting area to explore is how the models perform on specialized tasks like code generation and understanding, where their language understanding and generation capabilities could be leveraged in novel ways.
Updated Invalid Date
🏋️
Llama-3.2-1B-Instruct
262
The Llama-3.2-1B-Instruct model is part of the Meta Llama 3.2 collection of multilingual large language models (LLMs). It is a 1 billion parameter, pretrained and instruction-tuned generative model optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. The Llama-3.2-3B-Instruct model is a larger 3 billion parameter version in the same model family. Model inputs and outputs The Llama-3.2-1B-Instruct model takes multilingual text as input and generates multilingual text and code as output. It has a context length of 128k tokens. Inputs Multilingual text Outputs Multilingual text and code Capabilities The Llama 3.2 models are optimized for multilingual dialogue tasks, including assistive applications like knowledge retrieval and summarization. They outperform many open-source and closed-domain chat models on common industry benchmarks. What can I use it for? The Llama-3.2-1B-Instruct model is intended for commercial and research use in multiple languages. The instruction-tuned version is well-suited for applications like multilingual chatbots, writing assistants, and query/prompt rewriting. The pretrained version can also be adapted for a variety of other natural language generation tasks. Things to try Given the model's multilingual capabilities, you could experiment with using it for cross-lingual applications, such as translating between supported languages or generating content in multiple languages. The model's strong performance on tasks like summarization also makes it interesting to try for content creation and analysis use cases.
Updated Invalid Date
🧪
Llama-3.2-3B-Instruct
235
The Llama-3.2-3B-Instruct model is part of the Meta Llama 3.2 collection of multilingual large language models (LLMs). It is a pretrained and instruction-tuned generative model optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. The Llama 3.2 models outperform many available open-source and closed chat models on common industry benchmarks. This 3B parameter model is one of the smaller variants in the Llama 3.2 family, which also includes larger 1B and 8B versions. The Llama 3.2 models use an optimized transformer architecture and were trained using a combination of supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the models with human preferences for helpfulness and safety. Meta developed the Llama 3.2 models and they are available under the Llama 3.2 Community License. Model inputs and outputs Inputs Multilingual Text**: The Llama-3.2-3B-Instruct model accepts multilingual text as input, with official support for 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Multilingual Code**: In addition to natural language text, the model can also handle code inputs across these supported languages. Outputs Multilingual Text**: The model generates multilingual text responses in the supported languages. Multilingual Code**: The model can also generate code outputs in the supported languages. Capabilities The Llama-3.2-3B-Instruct model is capable of engaging in multilingual dialogue, answering questions, summarizing information, and performing a variety of other natural language processing tasks. Its instruction-tuning allows it to follow prompts and execute commands in a helpful and reliable manner. The model has also demonstrated strong performance on benchmarks testing reasoning, commonsense understanding, and other cognitive capabilities. What can I use it for? The Llama-3.2-3B-Instruct model is intended for commercial and research use in multiple languages. Its instruction-tuned text-only capabilities make it well-suited for building multilingual assistant applications, chatbots, and other dialogue-based systems. Developers can also fine-tune the model for a variety of other natural language generation tasks, such as text summarization, language translation, and content creation. Things to try One interesting aspect of the Llama-3.2-3B-Instruct model is its ability to handle code inputs and outputs. Developers could experiment with using the model to generate, explain, or modify code snippets in the supported languages. Another intriguing possibility is leveraging the model's multilingual capabilities to build cross-lingual applications, where users can seamlessly interact in their preferred language.
Updated Invalid Date
🤿
Llama-3.1-8B
902
The Llama-3.1-8B is a part of the Meta Llama 3.1 collection of multilingual large language models (LLMs) developed by Meta. This collection includes models in 8B, 70B, and 405B parameter sizes, all of which are optimized for multilingual dialogue use cases. The Llama-3.1-8B model specifically is an auto-regressive language model that uses an optimized transformer architecture and has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. The Llama-3.1-8B model is part of a family of similar Llama 3.1 models, including the Llama-3.1-405B and Meta-Llama-3.1-70B models. All of these models were developed by the meta-llama team at Meta. Model inputs and outputs Inputs Multilingual Text**: The Llama-3.1-8B model supports multilingual text input in 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Multilingual Code**: The model can also take in code snippets in these 8 supported languages. Outputs Multilingual Text**: The model can generate multilingual text output in the 8 supported languages. Multilingual Code**: The model can also generate code snippets in the 8 supported languages. Capabilities The Llama-3.1-8B model is capable of performing a variety of natural language generation tasks, such as open-ended dialogue, question answering, and text summarization. It has been shown to outperform many available open source and closed chat models on common industry benchmarks. The model's multilingual capabilities make it particularly useful for applications that need to communicate in multiple languages. What can I use it for? The Llama-3.1-8B model is intended for commercial and research use in multiple languages. The instruction-tuned text-only models like this one are well-suited for assistant-like chat applications, while the pretrained models can be adapted for a wider range of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models, such as through synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. Things to try One interesting aspect of the Llama-3.1-8B model is its ability to handle long-form context. With a context length of 128k tokens, the model can maintain coherence and consistency over extended dialogues or documents. Developers could explore using this capability to build more natural and engaging conversational AI assistants. Another area to experiment with is the model's multilingual capabilities. Since the Llama-3.1-8B supports 8 languages, developers could try fine-tuning or adapting the model for specific language domains or tasks in those languages. The Llama 3 paper discusses some of the techniques used to enable this multilingual functionality.
Updated Invalid Date