Yi-1.5-34B-Chat

Maintainer: 01-ai

Total Score

164

Last updated 6/7/2024

🏋️

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

Get summaries of the top AI models delivered straight to your inbox:

Model overview

Yi-1.5-34B-Chat is an upgraded version of the Yi language model, developed by the team at 01.AI. Compared to the original Yi model, Yi-1.5-34B-Chat has been continuously pre-trained on a high-quality corpus of 500B tokens and fine-tuned on 3M diverse samples. This allows it to deliver stronger performance in areas like coding, math, reasoning, and instruction-following, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. The model is available in several different sizes, including Yi-1.5-9B-Chat and Yi-1.5-6B-Chat, catering to different use cases and hardware constraints.

Model inputs and outputs

The Yi-1.5-34B-Chat model can accept a wide range of natural language inputs, including text prompts, instructions, and questions. It can then generate coherent and contextually appropriate responses, making it a powerful tool for conversational AI applications. The model's large scale and diverse training data allow it to engage in thoughtful discussions, provide detailed explanations, and even tackle complex tasks like coding and mathematical problem-solving.

Inputs

  • Natural language text prompts
  • Conversational queries and instructions
  • Requests for analysis, explanation, or task completion

Outputs

  • Coherent and contextually relevant responses
  • Detailed explanations and task completions
  • Creative and innovative solutions to open-ended problems

Capabilities

The Yi-1.5-34B-Chat model demonstrates impressive capabilities across a variety of domains. It excels at language understanding, commonsense reasoning, and reading comprehension, allowing it to engage in natural, context-aware conversations. The model also shines in areas like coding, math, and reasoning, where it can provide insightful solutions and explanations. Additionally, the model's strong instruction-following capability makes it well-suited for tasks that require following complex guidelines or steps.

What can I use it for?

The Yi-1.5-34B-Chat model has a wide range of potential applications, from conversational AI assistants and chatbots to educational tools and creative writing aids. Developers could leverage the model's language understanding and generation capabilities to build virtual assistants that can engage in natural, context-sensitive dialogues. Educators could use the model to create interactive learning experiences, providing personalized explanations and feedback to students. Businesses could explore using the model for customer service, content generation, or even internal task automation.

Things to try

One interesting aspect of the Yi-1.5-34B-Chat model is its ability to engage in open-ended, contextual reasoning. Users can provide the model with complex prompts or instructions and observe how it formulates thoughtful, creative responses. For example, you could ask the model to solve a challenging math problem, provide a detailed analysis of a historical event, or generate a unique story based on a given premise. The model's versatility and problem-solving skills make it a valuable tool for exploring the boundaries of conversational AI and language understanding.



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

Yi-1.5-9B-Chat

01-ai

Total Score

88

Yi-1.5-9B-Chat is an upgraded version of the Yi language model, developed by the team at 01-ai. Compared to the original Yi model, Yi-1.5-9B-Chat has been continuously pre-trained on a high-quality corpus of 500 billion tokens and fine-tuned on 3 million diverse samples. This allows the model to deliver stronger performance in areas like coding, math, reasoning, and instruction-following, while maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. The model has a context length of 4,096 tokens and has been pre-trained on 3.6 trillion tokens. Model inputs and outputs Yi-1.5-9B-Chat is a text-to-text model, meaning it takes textual input and generates textual output. The model can be used for a wide variety of natural language tasks, from open-ended chat to more specialized applications like code generation, mathematical problem-solving, and task completion. Inputs Freeform text prompts Instructions or commands Outputs Relevant and coherent textual responses Generated code, mathematical solutions, or task completions Capabilities Yi-1.5-9B-Chat exhibits strong performance across a range of benchmarks, often outperforming larger models in areas like commonsense reasoning and reading comprehension. The model has been shown to be particularly adept at following complex instructions, generating high-quality code, and solving mathematical problems. What can I use it for? The versatility of Yi-1.5-9B-Chat makes it suitable for a wide range of applications. Developers could use the model for tasks like code generation, automated programming, or building intelligent virtual assistants. Researchers could leverage the model's reasoning and problem-solving capabilities for tasks like mathematical modeling or scientific analysis. Businesses could explore using the model for customer service, content generation, or knowledge management applications. Things to try One interesting aspect of Yi-1.5-9B-Chat is its ability to engage in open-ended dialogue and provide thoughtful, contextual responses. Users could try prompting the model with complex questions or hypothetical scenarios and see how it responds. Additionally, the model's strong performance on tasks like reading comprehension and commonsense reasoning could make it useful for developing educational or training applications.

Read more

Updated Invalid Date

AI model preview image

yi-34b-chat

01-ai

Total Score

251

The yi-34b-chat model is a large language model trained from scratch by developers at 01.AI. The Yi series models are the next generation of open-source large language models that show promise in language understanding, commonsense reasoning, and reading comprehension. For example, the Yi-34B-Chat model landed in second place (following GPT-4 Turbo) on the AlpacaEval Leaderboard, outperforming other LLMs like GPT-4, Mixtral, and Claude. Similar models in the Yi series include the yi-6b and yi-34b models, which are also large language models trained by 01.AI. Other related models include the multilingual-e5-large text embedding model, the nous-hermes-2-yi-34b-gguf fine-tuned Yi-34B model, and the llava-13b visual instruction tuning model. Model Inputs and Outputs The yi-34b-chat model takes in a user prompt as input and generates a corresponding response. The input prompt can be a question, a statement, or any other text that the user wants the model to address. Inputs Prompt**: The text that the user wants the model to respond to. Temperature**: A value that controls the randomness of the model's output. Lower temperatures result in more focused and deterministic responses, while higher temperatures lead to more diverse and creative outputs. Top K**: The number of highest probability tokens to consider for generating the output. If > 0, only the top k tokens with the highest probability are kept (top-k filtering). Top P**: A probability threshold for generating the output. If = top_p are kept (nucleus filtering). Max New Tokens**: The maximum number of tokens the model should generate as output. Prompt Template**: A template used to format the input prompt, with the actual prompt inserted using the {prompt} placeholder. Repetition Penalty**: A value that penalizes the model for repeating the same tokens in the output. Outputs The model generates a response text based on the provided input. The output can be a single sentence, a paragraph, or multiple paragraphs, depending on the complexity of the input prompt. Capabilities The yi-34b-chat model demonstrates impressive capabilities in areas such as language understanding, commonsense reasoning, and reading comprehension. It has been shown to outperform other large language models in various benchmarks, including the AlpacaEval Leaderboard. What Can I Use It For? The yi-34b-chat model can be used for a wide range of applications, including: Conversational AI**: The model can be used to build chatbots and virtual assistants that can engage in natural language conversations. Content Generation**: The model can be used to generate text content, such as articles, stories, or product descriptions. Question Answering**: The model can be used to answer a variety of questions, drawing upon its strong language understanding and reasoning capabilities. Summarization**: The model can be used to summarize long passages of text, capturing the key points and main ideas. Code Generation**: The model can be used to assist developers by generating code snippets or even entire programs based on natural language prompts. Things to Try One interesting aspect of the yi-34b-chat model is its ability to generate diverse and creative responses. By adjusting the temperature and other parameters, you can explore the model's versatility and see how it responds to different types of prompts. You can also try fine-tuning the model on your own dataset to customize its capabilities for your specific use case. Another interesting aspect is the model's strong performance in commonsense reasoning and reading comprehension tasks. You can experiment with prompts that require the model to draw inferences, solve problems, or demonstrate its understanding of complex concepts. Overall, the yi-34b-chat model offers a powerful and flexible platform for exploring the capabilities of large language models and developing innovative applications.

Read more

Updated Invalid Date

AI model preview image

yi-6b-chat

01-ai

Total Score

3

The yi-6b-chat is one of the Yi series models, a set of large language models trained from scratch by developers at 01.AI. The Yi series models are targeted as bilingual language models and trained on a 3T multilingual corpus, making them one of the strongest LLMs worldwide. For example, the Yi-34B-Chat model landed in second place (following GPT-4 Turbo) on the AlpacaEval Leaderboard, outperforming other LLMs like GPT-4, Mixtral, and Claude. The Yi-6B-Chat model is a 6 billion parameter chat-oriented model, suitable for personal and academic use cases. It was trained on 3T of pretraining data, with a default context window of 4K tokens. Like other Yi models, the Yi-6B-Chat adopts the Transformer architecture and can leverage the existing Llama ecosystem. Model inputs and outputs The Yi-6B-Chat model takes a prompt as input and generates a text response as output. The prompt can be a conversational query, a task instruction, or any other natural language text. The model's output is not constrained to a specific format and can range from a short phrase to multiple paragraphs of relevant and coherent text. Inputs Prompt**: The input text that the model will use to generate a response. Outputs Response**: The text generated by the model based on the input prompt. Capabilities The Yi-6B-Chat model demonstrates strong capabilities in language understanding, generation, and reasoning. It can engage in open-ended conversations, answer questions, provide task-oriented assistance, and even generate creative content. The model's performance is particularly noteworthy in areas like common-sense reasoning, reading comprehension, and code generation. What can I use it for? The Yi-6B-Chat model can be used for a wide range of applications, including: Chatbots and virtual assistants**: Leverage the model's conversational abilities to build chatbots and virtual assistants that can engage in natural language interactions. Content generation**: Use the model to generate text for things like articles, stories, scripts, and more. Question answering**: Utilize the model's language understanding and reasoning capabilities to build question-answering systems. Task-oriented assistance**: Employ the model to provide help with specific tasks, such as code generation, analysis, and problem-solving. Things to try One interesting aspect of the Yi-6B-Chat model is its ability to generate coherent and diverse responses. Unlike some chat models that may produce repetitive or predictable outputs, the Yi-6B-Chat can provide varied and creative responses to the same prompt. This can be particularly useful for scenarios that require a more open-ended and flexible approach, such as creative writing or open-ended problem-solving. Another intriguing feature of the Yi-6B-Chat is its strong performance in common-sense reasoning and reading comprehension tasks. This makes the model a valuable asset for applications that require deeper understanding of context and the ability to draw inferences, such as question-answering systems or intelligent tutoring systems.

Read more

Updated Invalid Date

AI model preview image

yi-34b

01-ai

Total Score

2

The yi-34b model is a large language model trained from scratch by developers at 01.AI. The Yi series models are the next generation of open-source large language models that demonstrate strong performance across a variety of benchmarks, including language understanding, commonsense reasoning, and reading comprehension. Similar models like multilingual-e5-large and llava-13b also aim to provide powerful multilingual or visual language modeling capabilities. However, the Yi-34B model stands out for its exceptional performance, ranking second only to GPT-4 Turbo on the AlpacaEval Leaderboard and outperforming other LLMs like GPT-4, Mixtral, and Claude. Model inputs and outputs The yi-34b model is a large language model that can be used for a variety of natural language processing tasks, such as text generation, question answering, and language understanding. Inputs Prompt**: The input text that the model uses to generate output. Top K**: The number of highest probability tokens to consider for generating the output. Top P**: A probability threshold for generating the output. Temperature**: The value used to modulate the next token probabilities. Max New Tokens**: The maximum number of tokens the model should generate as output. Outputs The model generates output text in response to the provided prompt. Capabilities The yi-34b model demonstrates strong performance across a range of benchmarks, including language understanding, commonsense reasoning, and reading comprehension. For example, the Yi-34B-Chat model ranked second on the AlpacaEval Leaderboard, outperforming other large language models like GPT-4, Mixtral, and Claude. Additionally, the Yi-34B model ranked first among all existing open-source models on the Hugging Face Open LLM Leaderboard and C-Eval, both in English and Chinese. What can I use it for? The yi-34b model is well-suited for a variety of applications, from personal and academic use to commercial applications, particularly for small and medium-sized enterprises. Its strong performance and cost-effective solution make it a viable option for tasks such as language generation, question answering, and text summarization. Things to try One interesting thing to try with the yi-34b model is exploring its capabilities in code generation and mathematical problem-solving. According to the provided benchmarks, the Yi-9B model, a smaller version of the Yi series, demonstrated exceptional performance in these areas, outperforming several similar-sized open-source models. By fine-tuning the yi-34b model on relevant datasets, you may be able to unlock even more powerful capabilities for these types of tasks.

Read more

Updated Invalid Date