ELYZA-japanese-Llama-2-7b-instruct

Maintainer: elyza

Total Score

53

Last updated 5/27/2024

๐Ÿ…

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The ELYZA-japanese-Llama-2-7b-instruct model is a 6.27 billion parameter language model developed by elyza for natural language processing tasks. It is based on the Llama 2 architecture and has been fine-tuned on a Japanese dataset to improve its performance on Japanese-language tasks. The model is available through the Hugging Face platform and is intended for commercial and research use.

Model inputs and outputs

Inputs

  • The model takes in Japanese text as input.

Outputs

  • The model generates Japanese text as output.

Capabilities

The ELYZA-japanese-Llama-2-7b-instruct model is capable of a variety of natural language processing tasks, such as text generation, question answering, and language translation. It has been shown to perform well on benchmarks evaluating commonsense reasoning, world knowledge, and reading comprehension.

What can I use it for?

The ELYZA-japanese-Llama-2-7b-instruct model can be used for a wide range of applications, including chatbots, language generation, and machine translation. For example, a company could use the model to develop a Japanese-language virtual assistant that can engage in natural conversations and provide helpful information to users. Researchers could also use the model as a starting point for further fine-tuning and development of Japanese language models for specific domains or tasks.

Things to try

One interesting aspect of the ELYZA-japanese-Llama-2-7b-instruct model is its ability to handle longer input sequences, thanks to the rope_scaling option. Developers could experiment with using longer prompts to see if the model can generate more coherent and context-aware responses. Additionally, the model could be fine-tuned on domain-specific datasets to improve its performance on specialized tasks, such as legal document summarization or scientific paper generation.



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

๐Ÿงช

ELYZA-japanese-Llama-2-7b-fast-instruct

elyza

Total Score

73

ELYZA-japanese-Llama-2-7b-fast-instruct is a large language model developed by elyza that is based on the Llama 2 architecture. It is one of several Japanese-focused Llama 2 models released by elyza, including the ELYZA-japanese-Llama-2-7b, ELYZA-japanese-Llama-2-7b-instruct, and ELYZA-japanese-Llama-2-7b-fast variants. These models are fine-tuned on Japanese data and optimized for different use cases, with the fast-instruct version targeting efficient instruction-following performance. Model inputs and outputs Inputs The model takes in text prompts as input, which can be in Japanese or other supported languages. Outputs The model generates text outputs in response to the input prompts, which can be used for a variety of natural language processing tasks such as language generation, question answering, and code generation. Capabilities The ELYZA-japanese-Llama-2-7b-fast-instruct model has been optimized for efficient instruction-following, allowing it to quickly generate relevant and coherent responses to prompts. Its Japanese-focused training also gives it strong capabilities in understanding and generating Japanese text. What can I use it for? The ELYZA-japanese-Llama-2-7b-fast-instruct model could be useful for a variety of applications that require Japanese language generation or understanding, such as chatbots, virtual assistants, or language learning tools. Its instruction-following capabilities make it well-suited for tasks like code generation, task automation, or interactive question answering. Things to try You could try prompting the model with a variety of Japanese language tasks, such as translating between Japanese and other languages, answering questions about Japanese culture or history, or generating creative Japanese-language stories or poems. Its efficient instruction-following capabilities also make it an interesting model to experiment with for automating workflows or generating code in Japanese-speaking contexts.

Read more

Updated Invalid Date

๐ŸŽฒ

ELYZA-japanese-Llama-2-7b

elyza

Total Score

79

The ELYZA-japanese-Llama-2-7b is a large language model based on the Llama 2 architecture developed by Meta. It has been fine-tuned by elyza to work with Japanese language inputs and outputs. Similar models in the ELYZA-japanese-Llama-2-7b series include the ELYZA-japanese-Llama-2-7b-instruct, ELYZA-japanese-Llama-2-7b-fast, and ELYZA-japanese-Llama-2-7b-fast-instruct models, which offer different capabilities and performance characteristics. Model inputs and outputs Inputs The ELYZA-japanese-Llama-2-7b model accepts Japanese language text as input. Outputs The model generates Japanese language text in response to the input. Capabilities The ELYZA-japanese-Llama-2-7b model is capable of a variety of natural language processing tasks, such as text generation, language translation, and question answering. Its fine-tuning on Japanese data allows it to perform well on tasks requiring understanding and generation of Japanese text. What can I use it for? The ELYZA-japanese-Llama-2-7b model could be useful for a range of applications, including: Developing Japanese language chatbots or virtual assistants Translating between Japanese and other languages Generating Japanese text for content creation or summarization Answering questions or providing information in the Japanese language Things to try One interesting aspect of the ELYZA-japanese-Llama-2-7b model is its potential for generating coherent and contextually appropriate Japanese text. Developers could experiment with prompting the model to write short stories, poems, or even news articles in Japanese to see the quality and creativity of the output.

Read more

Updated Invalid Date

๐ŸŒ€

llama-2-coder-7b

mrm8488

Total Score

51

The llama-2-coder-7b model is a 7 billion parameter large language model (LLM) fine-tuned on the CodeAlpaca 20k instructions dataset using the QLoRA method. It is similar to other fine-tuned LLMs like the FalCoder 7B model, which was also fine-tuned on the CodeAlpaca dataset. The llama-2-coder-7b model was developed by mrm8488, a Hugging Face community contributor. Model inputs and outputs Inputs The llama-2-coder-7b model takes in text prompts as input, typically in the form of instructions or tasks that the model should try to complete. Outputs The model generates text as output, providing a solution or response to the given input prompt. The output is designed to be helpful and informative for coding-related tasks. Capabilities The llama-2-coder-7b model has been fine-tuned to excel at following programming-related instructions and generating relevant code solutions. For example, the model can be used to design a class for representing a person in Python, or to solve various coding challenges and exercises. What can I use it for? The llama-2-coder-7b model can be a valuable tool for developers, students, and anyone interested in improving their coding skills. It can be used for tasks such as: Generating code solutions to programming problems Explaining coding concepts and techniques Providing code reviews and suggestions for improvement Assisting with prototyping and experimenting with new ideas Things to try One interesting thing to try with the llama-2-coder-7b model is to provide it with open-ended prompts or challenges and see how it responds. The model's ability to understand and generate relevant code solutions can be quite impressive, and experimenting with different types of inputs can reveal the model's strengths and limitations. Additionally, comparing the llama-2-coder-7b model's performance to other fine-tuned LLMs, such as the FalCoder 7B model, can provide insights into the unique capabilities of each model.

Read more

Updated Invalid Date

๐Ÿ“Š

Llama-2-ko-7b-Chat

kfkas

Total Score

66

Llama-2-ko-7b-Chat is an AI model developed by Taemin Kim (kfkas) and Juwon Kim (uomnf97). It is based on the LLaMA model and has been fine-tuned on the nlpai-lab/kullm-v2 dataset for chat-based applications. Model inputs and outputs Inputs Models input text only. Outputs Models generate text only. Capabilities Llama-2-ko-7b-Chat can engage in open-ended conversations, answering questions, and providing information on a wide range of topics. It has been trained to be helpful, respectful, and informative in its responses. What can I use it for? The Llama-2-ko-7b-Chat model can be used for building conversational AI applications, such as virtual assistants, chatbots, and interactive learning experiences. Its strong language understanding and generation capabilities make it well-suited for tasks like customer service, tutoring, and knowledge sharing. Things to try One interesting aspect of Llama-2-ko-7b-Chat is its ability to provide detailed, step-by-step instructions for tasks. For example, you could ask it to guide you through the process of planning a camping trip, and it would generate a comprehensive list of essential items to bring and tips for a safe and enjoyable experience.

Read more

Updated Invalid Date