yi-34b-200k
Maintainer: 01-ai
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| Model Link | View on Replicate |
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| Paper Link | No paper link provided |
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Model overview
The yi-34b is a large language model trained from scratch by developers at 01.AI. It is part of the Yi series models, which are targeted as bilingual language models and trained on a 3T multilingual corpus. The Yi series models show promise in language understanding, commonsense reasoning, reading comprehension, and more.
The yi-34b-chat is a chat model based on the yi-34b base model, which has been fine-tuned using a Supervised Fine-Tuning (SFT) approach. This results in responses that mirror human conversation style more closely compared to the base model.
The yi-6b is a smaller version of the Yi series models, with a parameter size of 6 billion. It is suitable for personal and academic use.
Model inputs and outputs
The Yi models accept natural language prompts as input and generate continuations of the prompt as output. The models 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 should use to generate a continuation.
- Temperature: A value that controls the "creativity" of the model's outputs, with higher values generating more diverse and unpredictable text.
- Top K: The number of highest probability tokens to consider for generating the output.
- Top P: A probability threshold for generating the output, keeping only the top tokens with cumulative probability above the threshold.
Outputs
- Generated text: The model's continuation of the input prompt, generated token-by-token.
Capabilities
The Yi series models, particularly the yi-34b and yi-34b-chat, have demonstrated impressive performance on a range of benchmarks. The yi-34b-chat model ranked second on the AlpacaEval Leaderboard, outperforming other large language models like GPT-4, Mixtral, and Claude.
The yi-34b and yi-34b-200K models have also performed exceptionally well on the Hugging Face Open LLM Leaderboard (pre-trained) and C-Eval, ranking first among all existing open-source models in both English and Chinese.
What can I use it for?
The Yi series models can be used for a variety of natural language processing tasks, such as:
- Content generation: The models can be used to generate diverse and engaging text, including stories, articles, and poems.
- Question answering: The models can be used to answer questions on a wide range of topics, drawing on their broad knowledge base.
- Language understanding: The models can be used to analyze and understand natural language, with applications in areas like sentiment analysis and text classification.
Things to try
One interesting thing to try with the Yi models is to experiment with different input prompts and generation parameters to see how the models respond. For example, you could try prompting the models with open-ended questions or creative writing prompts, and observe the diverse range of responses they generate.
You could also explore the models' capabilities in specialized domains, such as code generation or mathematical problem-solving, by providing them with relevant prompts and evaluating their performance.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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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.
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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.
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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.
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