Falcon-180B-Chat-GGUF
Maintainer: TheBloke
122
🔄
| 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 Falcon-180B-Chat-GGUF is a large language model developed by Technology Innovation Institute and maintained by TheBloke. It is a version of the Falcon-180B model that has been fine-tuned for chat and dialogue tasks. This model is available in a GGUF format, which is a new file format introduced by the llama.cpp team that offers several advantages over the previous GGML format. The GGUF files are compatible with a variety of AI tools and libraries, including llama.cpp, text-generation-webui, and ctransformers.
Model inputs and outputs
Inputs
- Text prompts
Outputs
- Generating human-like responses to text prompts
Capabilities
The Falcon-180B-Chat-GGUF model is capable of engaging in open-ended conversations, answering questions, and generating coherent and relevant text in response to a wide range of prompts. It has been fine-tuned on chat datasets to improve its dialogue capabilities and align its responses with human preferences for helpfulness and safety.
What can I use it for?
The Falcon-180B-Chat-GGUF model can be used for a variety of natural language processing tasks, such as building chatbots, virtual assistants, or other conversational AI applications. Its large scale and fine-tuning on chat data make it well-suited for use cases that require engaging and helpful responses, such as customer service, educational applications, or general-purpose dialogue systems.
Things to try
One interesting thing to try with the Falcon-180B-Chat-GGUF model is to experiment with different prompting techniques to see how it responds in different contexts. For example, you could try providing the model with open-ended prompts, task-oriented instructions, or even creative writing prompts to see the diversity of its responses. Additionally, you could explore the model's capabilities in terms of following up on previous statements, maintaining context, and adapting its tone and personality to the user's needs.
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-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
🖼️
Llama-2-7B-Chat-GGUF
377
The Llama-2-7B-Chat-GGUF model is a 7 billion parameter large language model created by Meta. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are designed for dialogue use cases and have been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align them to human preferences for helpfulness and safety. Compared to open-source chat models, the Llama-2-Chat models outperform on many benchmarks and are on par with some popular closed-source models like ChatGPT and PaLM in human evaluations. The model is maintained by TheBloke, who has generously provided GGUF format versions of the model with various quantization levels to enable efficient CPU and GPU inference. Similar GGUF models are also available for the larger 13B and 70B versions of the Llama 2 model. Model inputs and outputs Inputs Text**: The model takes text prompts as input, which can be anything from a single question to multi-turn conversational exchanges. Outputs Text**: The model generates text continuations in response to the input prompt. This can range from short, concise responses to more verbose, multi-sentence outputs. Capabilities The Llama-2-7B-Chat-GGUF model is capable of engaging in open-ended dialogue, answering questions, and generating text on a wide variety of topics. It demonstrates strong performance on tasks like commonsense reasoning, world knowledge, reading comprehension, and mathematical problem solving. Compared to earlier versions of the Llama model, the Llama 2 chat models also show improved safety and alignment with human preferences. What can I use it for? The Llama-2-7B-Chat-GGUF model can be used for a variety of natural language processing tasks, such as building chatbots, question-answering systems, text summarization tools, and creative writing assistants. Given its strong performance on benchmarks, it could be a good starting point for building more capable AI assistants. The quantized GGUF versions provided by TheBloke also make the model accessible for deployment on a wide range of hardware, from CPUs to GPUs. Things to try One interesting thing to try with the Llama-2-7B-Chat-GGUF model is to engage it in multi-turn dialogues and observe how it maintains context and coherence over the course of a conversation. You could also experiment with providing the model with prompts that require reasoning about hypotheticals or abstract concepts, and see how it responds. Additionally, you could try fine-tuning or further training the model on domain-specific data to see if you can enhance its capabilities for particular applications.
Updated Invalid Date
🤖
Llama-2-13B-chat-GGUF
185
The Llama-2-13B-chat-GGUF model is a 13 billion parameter large language model created by TheBloke that is optimized for conversational tasks. It is based on Meta's Llama 2 model, which is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. TheBloke has provided GGUF format model files, which is a new format introduced by the llama.cpp team on August 21st 2023 that supersedes the previous GGML format. Similar models provided by TheBloke include the Llama-2-7B-Chat-GGML and Llama-2-13B-GGML models, which use the older GGML format. TheBloke has also provided a range of quantized versions of these models in both GGML and GGUF formats to optimize for performance on different hardware. Model inputs and outputs Inputs Text prompts**: The model accepts text prompts as input, which can include instructions, queries, or any other natural language text. Outputs Generated text**: The model outputs generated text, continuing the input prompt in a coherent and contextual manner. The output can be used for a variety of language generation tasks such as dialogue, story writing, and answering questions. Capabilities The Llama-2-13B-chat-GGUF model is particularly adept at conversational tasks, as it has been fine-tuned by TheBloke specifically for chat applications. It can engage in open-ended dialogues, answer follow-up questions, and provide helpful and informative responses. Compared to open-source chat models, the Llama-2-Chat series from Meta has been shown to outperform on many benchmarks and provide outputs that are on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. What can I use it for? The Llama-2-13B-chat-GGUF model can be used for a wide variety of language generation tasks, but it is particularly well-suited for building conversational AI assistants and chatbots. Some potential use cases include: Customer service chatbots**: Deploying the model as a virtual customer service agent to handle queries, provide information, and guide users through processes. Intelligent personal assistants**: Integrating the model into smart home devices, productivity apps, or other applications to provide a natural language interface. Dialogue systems**: Building interactive storytelling experiences, roleplaying games, or other applications that require fluent and contextual dialogue. Things to try One interesting aspect of the Llama-2-Chat models is their ability to maintain context and engage in multi-turn dialogues. Try providing the model with a sequence of related prompts and see how it responds, building on the previous context. You can also experiment with different temperature and repetition penalty settings to adjust the creativity and coherence of the generated outputs. Another thing to explore is the model's performance on more specialized tasks, such as code generation, problem-solving, or creative writing. While the Llama-2-Chat models are primarily designed for conversational tasks, they may still demonstrate strong capabilities in these areas due to the breadth of their training data.
Updated Invalid Date
🏷️
Llama-2-70B-Chat-GGUF
119
The Llama-2-70B-Chat-GGUF model is a large language model developed by Meta Llama 2 and optimized for dialogue use cases. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. This model is the 70 billion parameter version, fine-tuned for chat and conversation tasks. It outperforms open-source chat models on most benchmarks, and in human evaluations, it is on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. Model inputs and outputs Inputs Text**: The model takes natural language text as input. Outputs Text**: The model generates natural language text as output, continuing the provided prompt. Capabilities The Llama-2-70B-Chat-GGUF model is capable of engaging in open-ended dialogue, answering questions, and generating coherent and contextually appropriate responses. It demonstrates strong performance on a variety of language understanding and generation tasks, including commonsense reasoning, world knowledge, reading comprehension, and mathematical problem-solving. What can I use it for? The Llama-2-70B-Chat-GGUF model can be used for a wide range of natural language processing tasks, such as chatbots, virtual assistants, content generation, and creative writing. Its large size and strong performance make it suitable for commercial and research applications that require advanced language understanding and generation capabilities. However, as with all large language models, care must be taken to ensure its outputs are safe and aligned with human values. Things to try One interesting thing to try with the Llama-2-70B-Chat-GGUF model is to engage it in open-ended conversations and observe how it maintains context, coherence, and appropriate tone and personality over extended interactions. Its performance on tasks that require reasoning about social dynamics, empathy, and nuanced communication can provide valuable insights into the current state of language model technology.
Updated Invalid Date