c4ai-command-r-v01-GGUF
Maintainer: andrewcanis
60
🌐
| Property | Value |
|---|---|
| Model Link | View 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 c4ai-command-r-v01-GGUF is a large language model created by CohereForAI and maintained by andrewcanis. This model is part of the Command-R 35B v1.0 series and is available in a quantized GGUF format for efficient CPU and GPU inference.
Similar models include the CausalLM-14B-GGUF and various CodeLlama models at different scales (7B, 13B, 34B, Instruct) created by Meta and maintained by TheBloke.
Model inputs and outputs
The c4ai-command-r-v01-GGUF model is a text-to-text transformer that takes in natural language text as input and generates relevant output text. The model can be used for a variety of natural language processing tasks such as language generation, text summarization, and question answering.
Inputs
- Natural language text prompts
Outputs
- Generated natural language text
Capabilities
The c4ai-command-r-v01-GGUF model has demonstrated strong performance on a variety of text-based tasks. It can be used to generate coherent and contextually relevant text, summarize long passages, and answer questions based on provided information. The model's broad capabilities make it a versatile tool for applications like content creation, language understanding, and task automation.
What can I use it for?
The c4ai-command-r-v01-GGUF model can be leveraged for a wide range of natural language processing applications, such as:
-
Automated content generation: Use the model to generate human-like text for blog posts, articles, product descriptions, and more. The model's ability to understand context and produce coherent output makes it well-suited for content creation tasks.
-
Text summarization: Summarize lengthy documents or reports by providing the model with the full text and having it generate concise, salient summaries.
-
Question answering: Supply the model with questions and relevant context, and it can provide informative answers based on the provided information.
-
Dialogue systems: Integrate the model into chatbots or virtual assistants to enable natural, contextual conversations with users.
-
Code generation: Leverage the model's broad language understanding capabilities to assist with programming tasks, such as generating code snippets or completing partially written code.
Things to try
One interesting aspect of the c4ai-command-r-v01-GGUF model is its ability to adapt to different prompting styles and task-specific fine-tuning. Experiment with various prompt formats, lengths, and styles to see how the model's output changes. Additionally, consider fine-tuning the model on domain-specific data to enhance its performance on your target use case.
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
⚙️
CodeLlama-7B-GGUF
99
The CodeLlama-7B-GGUF is a 7 billion parameter AI model created by Meta and maintained by TheBloke. It is part of the "Code Llama" family of models designed for code synthesis and understanding tasks. The model is available in GGUF format, a new model file format introduced by the llama.cpp team that offers advantages over the previous GGML format. Similar models include the CodeLlama-7B-Instruct-GGUF, which is optimized for instruction following and safer deployment, and the Llama-2-7B-GGUF, which is part of Meta's Llama 2 family of models. Model inputs and outputs Inputs Text inputs only Outputs Generated text outputs Capabilities The CodeLlama-7B-GGUF model is capable of a variety of code-related tasks, including code completion, infilling, and general understanding. It can handle a range of programming languages and is particularly well-suited for Python. What can I use it for? The CodeLlama-7B-GGUF model can be used for a variety of applications, such as building code assistants, automating code generation, and enhancing code understanding. Developers could integrate the model into their tools and workflows to improve productivity and efficiency. Companies working on AI-powered programming environments could also leverage the model to enhance their offerings. Things to try One interesting aspect of the CodeLlama-7B-GGUF model is its ability to handle extended sequence lengths, thanks to the GGUF format's support for RoPE scaling parameters. This could allow for more complex and contextual code generation tasks. Developers could experiment with prompts that require the model to generate or understand code across multiple lines or even files.
Updated Invalid Date
📈
CausalLM-14B-GGUF
116
The CausalLM-14B-GGUF is a 14B parameter language model created by CausalLM and quantized into the GGUF format by TheBloke. This model was generously supported by a grant from andreessen horowitz (a16z). It is similar in scale and capabilities to other large language models like Llama-2-13B-chat-GGUF and Llama-2-7B-Chat-GGUF, also quantized by TheBloke. Model inputs and outputs The CausalLM-14B-GGUF is a text-to-text model, taking text as input and generating text as output. It can be used for a variety of natural language processing tasks. Inputs Unconstrained free-form text input Outputs Unconstrained free-form text output Capabilities The CausalLM-14B-GGUF model is a powerful language model capable of generating human-like text. It can be used for tasks like language translation, text summarization, question answering, and creative writing. The model has been optimized for safety and helpfulness, making it suitable for use in conversational AI assistants. What can I use it for? You can use the CausalLM-14B-GGUF model for a wide range of natural language processing tasks. Some potential use cases include: Building conversational AI assistants Automating content creation for blogs, social media, and marketing materials Enhancing customer service chatbots Developing language learning applications Improving text summarization and translation Things to try One interesting thing to try with the CausalLM-14B-GGUF model is using it for open-ended creative writing. The model's ability to generate coherent and imaginative text can be a great starting point for story ideas, poetry, or other creative projects. You can also experiment with fine-tuning the model on specific datasets or prompts to tailor its capabilities for your needs.
Updated Invalid Date
🔎
CodeLlama-34B-GGUF
55
The CodeLlama-34B-GGUF is a 34 billion parameter large language model created by Meta and maintained by TheBloke. It is part of the CodeLlama family of models, which also includes 7B and 13B versions. The CodeLlama models are designed for code synthesis and understanding, with variants specialized for Python and instruction following. This 34B GGUF version provides quantized model files for efficient CPU and GPU inference. Model inputs and outputs Inputs Text**: The model takes text inputs to generate new text. Outputs Text**: The model outputs generated text, which can be used for a variety of tasks such as code completion, infilling, and chat. Capabilities The CodeLlama-34B-GGUF model is capable of general code synthesis and understanding. It can be used for tasks like code completion, where it can generate the next lines of code based on a prompt, as well as code infilling, where it can fill in missing parts of code. The model also has capabilities for instruction following and chat, making it useful for building AI assistants. What can I use it for? The CodeLlama-34B-GGUF model can be used for a variety of applications, such as building code editors or AI programming assistants. Developers could use the model to autocomplete code, generate new functions or classes, or explain code snippets. The instruction-following capabilities also make it useful for building chatbots or virtual assistants that can help with programming tasks. Things to try One interesting thing to try with the CodeLlama-34B-GGUF model is to provide it with a partially completed code snippet and see how it can fill in the missing parts. You could also try giving it a high-level description of a programming task and see if it can generate the necessary code to solve the problem. Additionally, you could experiment with using the model for open-ended conversations about programming concepts and techniques.
Updated Invalid Date
🎲
Llama-2-7B-GGUF
163
The Llama-2-7B-GGUF model is a text-to-text AI model created by TheBloke. It is based on Meta's Llama 2 7B model and has been converted to the new GGUF format. GGUF offers advantages over the previous GGML format, including better tokenization and support for special tokens. The model has also been made available in a range of quantization formats, from 2-bit to 8-bit, which trade off model size, inference speed, and quality. These include versions using the new "k-quant" methods developed by the llama.cpp team. The different quantized models are provided by TheBloke on Hugging Face. Other similar GGUF models include the Llama-2-13B-Chat-GGUF and Llama-2-7B-Chat-GGUF, which are fine-tuned for chat tasks. Model inputs and outputs Inputs Text**: The model takes natural language text as input. Outputs Text**: The model generates natural language text as output. Capabilities The Llama-2-7B-GGUF model is a powerful text generation model capable of a wide variety of tasks. It can be used for tasks like summarization, translation, question answering, and more. The model's performance has been evaluated on standard benchmarks and it performs well, particularly on tasks like commonsense reasoning and world knowledge. What can I use it for? The Llama-2-7B-GGUF model could be useful for a range of applications, such as: Content generation**: Generating news articles, product descriptions, creative stories, and other text-based content. Language understanding**: Powering chatbots, virtual assistants, and other natural language interfaces. Text summarization**: Automatically summarizing long documents or articles. Question answering**: Building systems that can answer questions on a variety of topics. The different quantized versions of the model provide options to balance model size, inference speed, and quality depending on the specific requirements of your application. Things to try One interesting thing to try with the Llama-2-7B-GGUF model is to fine-tune it on a specific domain or task using the training data and methods described in the Llama-2: Open Foundation and Fine-tuned Chat Models research paper. This could allow you to adapt the model to perform even better on your particular use case. Another idea is to experiment with prompting techniques to get the model to generate more coherent and contextually-relevant text. The model's performance can be quite sensitive to the way the prompt is structured, so trying different prompt styles and templates could yield interesting results.
Updated Invalid Date