grok-1-hf
Maintainer: keyfan
41
๐งช
| 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 grok-1-hf model is an unofficial dequantized version of the Grok-1 open-weights model, made available in the HuggingFace Transformers format by maintainer keyfan. Grok-1 is a large language model developed by xAI, which can be used for a variety of natural language processing tasks. The grok-1 model itself is available on HuggingFace, while hpcai-tech has created a PyTorch version with parallelism support, and Arki05 has provided GGUF quantized versions compatible with llama.cpp.
Model inputs and outputs
The grok-1-hf model is a text-to-text transformer model, meaning it takes text as input and generates text as output. It can be used for a variety of natural language processing tasks such as language modeling, text generation, and question answering.
Inputs
- Text: The model takes text as input, which can be in the form of a single sentence, a paragraph, or multiple paragraphs.
Outputs
- Text: The model generates text as output, which can be in the form of a continuation of the input text, a response to a question, or a completely new piece of text.
Capabilities
The grok-1-hf model has been shown to perform well on a variety of benchmarks, including the MMLU (Multi-Model Language Understanding) and BBH (Biased Behavioral Heterogeneity) datasets, where it achieved 0.7166 and 0.5204 5-shot accuracy respectively.
What can I use it for?
The grok-1-hf model could be useful for a variety of natural language processing tasks, such as language modeling, text generation, question answering, and more. For example, you could use the model to generate coherent and contextually relevant text, answer questions based on provided information, or even assist with tasks like creative writing or summarization.
Things to try
One interesting aspect of the grok-1-hf model is its ability to handle a diverse range of topics and tasks. You could try using the model to generate text on a wide variety of subjects, from creative fiction to technical documentation, and see how it performs. Additionally, you could experiment with different prompting strategies or fine-tuning the model on specific datasets to further enhance its capabilities for your particular 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
๐ฏ
grok-1
2.1K
grok-1 is an open-weights model created by xai-org, a leading organization in the field of artificial intelligence. This model is similar to other text-to-text models like openchat-3.5-1210 and openchat-3.5-0106, which are also large language models fine-tuned on a variety of high-quality instruction datasets. However, grok-1 differs in that it has an extremely large 314B parameter count, making it one of the largest open-source models available. Model inputs and outputs grok-1 is a text-to-text model, meaning it takes natural language text as input and generates natural language text as output. The model can be used for a wide variety of language tasks, from open-ended chat to task-oriented question answering and code generation. Inputs Natural language text prompts, such as questions, instructions, or open-ended statements Outputs Coherent natural language responses generated by the model based on the input prompt The model can output text of varying lengths, from short phrases to multi-paragraph responses Capabilities grok-1 demonstrates impressive capabilities across a range of language tasks. It can engage in open-ended dialogue, answer questions, summarize information, and even generate creative content like stories and poetry. The model's large size and diverse training data allow it to draw upon a vast amount of knowledge, making it a powerful tool for applications that require robust natural language understanding and generation. What can I use it for? Due to its impressive capabilities, grok-1 has a wide range of potential use cases. Developers and researchers could leverage the model for projects in areas like chatbots, virtual assistants, content generation, and language-based AI applications. Businesses could also explore using grok-1 to automate customer service tasks, generate marketing content, or provide intelligent information retrieval. Things to try One interesting aspect of grok-1 is its ability to handle long-form input and output. Try providing the model with detailed prompts or questions and see how it responds with coherent, substantive text. You could also experiment with using the model for creative writing tasks, such as generating story ideas or poetry. The model's large size and diverse training data make it a powerful tool for exploring the limits of natural language generation.
Updated Invalid Date
๐ฏ
grok-1
69
The grok-1 model, developed by the hpcai-tech team, is a PyTorch version of the original Grok-1 open-weights model released by xAI. This model has been translated from the original JAX version and includes a transformers-compatible tokenizer contributed by Xenova and ArthurZ. The model applies parallelism techniques from the ColossalAI framework to accelerate inference. Model inputs and outputs The grok-1 model is a text-to-text model, meaning it takes text as input and generates text as output. The model uses the standard Transformer architecture and can be used for a variety of natural language processing tasks. Inputs Text**: The model takes a text sequence as input, which can be a sentence, paragraph, or longer text. Outputs Generated Text**: The model outputs a sequence of generated text, which can be used for tasks like language generation, summarization, or translation. Capabilities The grok-1 model is capable of generating human-like text that can be used for a variety of applications. It has been shown to perform well on tasks like natural language inference, question answering, and text classification, as evidenced by its performance on benchmarks like SNLI, MNLI, and GLUE. What can I use it for? The grok-1 model can be used for a variety of natural language processing tasks, including: Text Generation**: The model can be used to generate human-like text, which can be useful for applications like dialog systems, creative writing, and content generation. Summarization**: The model can be fine-tuned to generate concise summaries of longer text, which can be useful for tasks like document summarization. Translation**: The model can be fine-tuned to translate text from one language to another, which can be useful for multilingual applications. Things to try One interesting thing to try with the grok-1 model is to use it in a few-shot or zero-shot learning scenario, where the model is asked to perform a task it wasn't explicitly trained for. This can help to evaluate the model's ability to generalize to new tasks and domains. Additionally, users can experiment with different generation settings, such as temperature and top-k sampling, to explore the range of text the model can generate.
Updated Invalid Date
โ๏ธ
mistral-7b-grok
43
The mistral-7b-grok model is a fine-tuned version of the mistralai/Mistral-7B-v0.1 model that has been aligned via Constitutional AI to mimic the style of xAI's Grok assistant. This model was developed by HuggingFaceH4. The model has been trained to achieve a loss of 0.9348 on the evaluation set, indicating strong performance. However, details about the model's intended uses and limitations, as well as the training and evaluation data, are not provided. Model Inputs and Outputs Inputs Text inputs for text-to-text tasks Outputs Transformed text outputs based on the input Capabilities The mistral-7b-grok model can be used for various text-to-text tasks, such as language generation, summarization, and translation. By mimicking the style of the Grok assistant, the model may be well-suited for conversational or interactive applications. What can I use it for? The mistral-7b-grok model could be used to develop interactive chatbots or virtual assistants that mimic the persona of the Grok assistant. This may be useful for customer service, educational applications, or entertainment purposes. The model could also be fine-tuned for specific text-to-text tasks, such as summarizing long-form content or translating between languages. Things to Try One interesting aspect of the mistral-7b-grok model is its ability to mimic the conversational style of the Grok assistant. Users could experiment with different prompts or conversation starters to see how the model responds and adapts its language to the desired persona. Additionally, the model could be evaluated on a wider range of tasks or benchmarks to better understand its capabilities and limitations.
Updated Invalid Date
๐
Grok-1-GGUF
61
The Grok-1-GGUF is an AI model created by Arki05 and available on the Hugging Face platform. It is a quantized version of the Grok-1 model, compatible with the llama.cpp library. The model has been split into multiple GGUF files for easier download and usage. GGUF is a new format introduced by the llama.cpp team, which offers improvements over the previous GGML format. Model inputs and outputs Inputs Text prompts Outputs Generated text continuations Capabilities The Grok-1-GGUF model is capable of performing natural language generation tasks. It can be used to continue text prompts, generate novel content, and engage in conversational interactions. The model has been quantized to various bit-widths, allowing for a trade-off between model size and quality. What can I use it for? The Grok-1-GGUF model can be used for a variety of natural language generation tasks, such as text completion, dialogue generation, and creative writing assistance. The model's capabilities make it suitable for applications like chatbots, content generation, and language exploration. Users can leverage the different quantization options to balance model size and performance based on their specific needs and hardware constraints. Things to try Users can experiment with the Grok-1-GGUF model by providing a wide range of text prompts and observing the generated outputs. The model's performance can be further evaluated on specific benchmarks or downstream tasks. Additionally, users can explore the impact of different quantization methods on the model's quality and efficiency, and choose the most suitable version for their use case.
Updated Invalid Date