falcon-180B-chat
Maintainer: tiiuae
529
👀
| 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
falcon-180B-chat is a 180B parameter causal decoder-only language model built by TII based on Falcon-180B and finetuned on a mixture of chat datasets including Ultrachat, Platypus, and Airoboros. It is made available under a permissive license allowing for commercial use.
Model inputs and outputs
falcon-180B-chat is a text-to-text model, meaning it takes text as input and generates text as output. The model is a causal decoder-only architecture, which means it can only generate text sequentially by predicting the next token based on the previous tokens.
Inputs
- Text prompts of any length, up to the model's maximum sequence length of 2048 tokens.
Outputs
- Continuation of the input text, generating new text that is coherent and relevant to the provided prompt.
Capabilities
The falcon-180B-chat model is one of the largest and most capable open-access language models available. It outperforms other prominent models like LLaMA-2, StableLM, RedPajama, and MPT according to the OpenLLM Leaderboard. It features an architecture optimized for inference, with multiquery attention.
What can I use it for?
The falcon-180B-chat model is well-suited for a variety of language-related tasks, such as text generation, chatbots, and dialogue systems. As a ready-to-use chat model based on the powerful Falcon-180B base, it can be a strong foundation for further finetuning and customization to specific use cases.
Things to try
Explore the model's capabilities by trying it on a variety of prompts and tasks. For example, see how it performs on open-ended conversations, question-answering, or task-oriented dialogues. You can also experiment with different decoding strategies, such as top-k sampling or beam search, to generate more diverse or controlled outputs.
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
💬
falcon-180B
1.1K
The falcon-180B is a massive 180 billion parameter causal decoder-only language model developed by the TII team. It was trained on an impressive 3.5 trillion tokens from the RefinedWeb dataset and other curated corpora. This makes it one of the largest open-access language models currently available. The falcon-180B builds upon the successes of earlier Falcon models like the Falcon-40B and Falcon-7B, incorporating architectural innovations like multiquery attention and FlashAttention for improved inference efficiency. It has demonstrated state-of-the-art performance, outperforming models like LLaMA, StableLM, RedPajama, and MPT according to the OpenLLM Leaderboard. Model inputs and outputs Inputs Text Prompts**: The falcon-180B model takes in free-form text prompts as input, which can be in a variety of languages including English, German, Spanish, and French. Outputs Generated Text**: Based on the input prompt, the model will generate coherent, contextually-relevant text continuations. The model can produce long-form passages, answer questions, and engage in open-ended dialogue. Capabilities The falcon-180B is an extraordinarily capable language model that can perform a wide range of natural language tasks. It excels at open-ended text generation, answering questions, and engaging in dialogue on a diverse array of topics. Given its massive scale, the model has impressive reasoning and knowledge retrieval abilities. What can I use it for? The falcon-180B model could be used as a foundation for building sophisticated AI applications across numerous domains. Some potential use cases include: Content Creation**: Generating creative written content like stories, scripts, articles, and marketing copy. Question Answering**: Building intelligent virtual assistants and chatbots that can engage in helpful, contextual dialogue. Research & Analysis**: Aiding in research tasks like literature reviews, hypothesis generation, and data synthesis. Code Generation**: Assisting with software development by generating code snippets and explaining programming concepts. Things to try One fascinating aspect of the falcon-180B is its ability to engage in open-ended reasoning and problem-solving. Try giving the model complex prompts that require multi-step logic, abstract thinking, or creative ideation. See how it tackles tasks that go beyond simple text generation, and observe the depth and coherence of its responses. Another interesting experiment is to fine-tune the falcon-180B on domain-specific data relevant to your use case. This can help the model develop specialized knowledge and capabilities tailored to your needs. Explore how the fine-tuned model performs compared to the base version.
Updated Invalid Date
🏅
Falcon-180B-Chat-GPTQ
67
The Falcon-180B-Chat-GPTQ model is a 180 billion parameter causal decoder-only language model created by Technology Innovation Institute. It is based on the original Falcon-180B model and fine-tuned on a mixture of chat datasets. This quantized GPTQ version provides a range of options to balance inference quality and VRAM usage. Compared to other large language models, Falcon-180B-Chat outperforms models like LLaMA-2, StableLM, and RedPajama according to the OpenLLM Leaderboard. Model inputs and outputs Inputs Text**: The Falcon-180B-Chat-GPTQ model takes text as input, which it uses to generate new text. Outputs Text**: The model outputs new text, continuing the provided input. Capabilities The Falcon-180B-Chat-GPTQ model is capable of generating human-like text across a variety of topics. It can engage in open-ended conversation, answer questions, and produce creative and coherent written content. The model's strong performance on benchmarks suggests it is one of the most capable open-source language models currently available. What can I use it for? The Falcon-180B-Chat-GPTQ model can be used for a wide range of natural language processing tasks, such as chatbots, question-answering systems, text summarization, and creative writing. Given its high performance, it could serve as a strong foundation for further fine-tuning and specialization to specific use cases. Developers and researchers may find it useful as a starting point for building advanced language AI applications. Things to try One interesting aspect of the Falcon-180B-Chat-GPTQ model is its ability to generate responses that maintain a consistent personality and tone, even across multiple exchanges. You could try providing the model with a short prompt that establishes a particular character or scenario, then see how it continues the conversation in a coherent and natural way. Another idea is to explore the model's performance on tasks that require reasoning, such as answering open-ended questions or solving simple logic problems - the model's strong performance on benchmarks suggests it may excel at these types of tasks as well.
Updated Invalid Date
⚙️
falcon-40b
2.4K
The falcon-40b is a 40 billion parameter causal decoder-only language model developed by TII. It was trained on 1,000 billion tokens of RefinedWeb enhanced with curated corpora. The falcon-40b outperforms other open-source models like LLaMA, StableLM, RedPajama, and MPT according to the OpenLLM Leaderboard. It features an architecture optimized for inference, with FlashAttention and multiquery. The falcon-40b is available under a permissive Apache 2.0 license, allowing for commercial use without royalties or restrictions. Model inputs and outputs Inputs Text**: The falcon-40b model takes text as input. Outputs Text**: The falcon-40b model generates text as output. Capabilities The falcon-40b is a powerful language model capable of a wide range of natural language processing tasks. It can be used for tasks like language generation, question answering, and text summarization. The model's strong performance on benchmarks suggests it could be useful for applications that require high-quality text generation. What can I use it for? With its large scale and robust performance, the falcon-40b model could be useful for a variety of applications. For example, it could be used to build AI writing assistants, chatbots, or content generation tools. Additionally, the model could be fine-tuned on domain-specific data to create specialized language models for fields like healthcare, finance, or research. The permissive license also makes the falcon-40b an attractive option for commercial use cases. Things to try One interesting aspect of the falcon-40b is its architecture optimized for inference, with FlashAttention and multiquery. This suggests the model may be able to generate text quickly and efficiently, making it well-suited for real-time applications. Developers could experiment with using the falcon-40b in low-latency scenarios, such as interactive chatbots or live content generation. Additionally, the model's strong performance on benchmarks indicates it may be a good starting point for further fine-tuning and customization. Researchers and practitioners could explore fine-tuning the falcon-40b on domain-specific data to create specialized language models for their particular use cases.
Updated Invalid Date
🛠️
falcon-7b
1.0K
The falcon-7b is a 7 billion parameter causal decoder-only language model developed by TII. It was trained on 1,500 billion tokens of the RefinedWeb dataset, which has been enhanced with curated corpora. The model outperforms comparable open-source models like MPT-7B, StableLM, and RedPajama on various benchmarks. Model Inputs and Outputs The falcon-7b model takes in text as input and generates text as output. It can be used for a variety of natural language processing tasks such as text generation, translation, and question answering. Inputs Raw text input Outputs Generated text output Capabilities The falcon-7b model is a powerful language model that can be used for a variety of natural language processing tasks. It has shown strong performance on various benchmarks, outperforming comparable open-source models. The model's architecture, which includes FlashAttention and multiquery, is optimized for efficient inference. What Can I Use It For? The falcon-7b model can be used as a foundation for further specialization and fine-tuning for specific use cases, such as text generation, chatbots, and content creation. Its permissive Apache 2.0 license also allows for commercial use without royalties or restrictions. Things to Try Developers can experiment with fine-tuning the falcon-7b model on their own datasets to adapt it to specific use cases. The model's strong performance on benchmarks suggests it could be a valuable starting point for building advanced natural language processing applications.
Updated Invalid Date