qwen1.5-72b
Maintainer: lucataco
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| Property | Value |
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| Model Link | View on Replicate |
| API Spec | View on Replicate |
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| Paper Link | No paper link provided |
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Model overview
qwen1.5-72b is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. It was created by lucataco. Similar models include the qwen1.5-110b, whisperspeech-small, phi-3-mini-4k-instruct, moondream2, and deepseek-vl-7b-base, all of which were also developed by lucataco.
Model inputs and outputs
qwen1.5-72b is a language model that generates text based on a given prompt. The model takes several inputs, including the prompt, system prompt, temperature, top-k and top-p sampling parameters, repetition penalty, max new tokens, and a random seed.
Inputs
- Prompt: The input text that the model will use to generate additional text.
- System Prompt: An optional prompt to set the overall behavior and personality of the model.
- Temperature: Controls the randomness of the generated text, with higher values leading to more diverse and unpredictable outputs.
- Top K: The number of most likely tokens to consider during sampling.
- Top P: The cumulative probability threshold to use for nucleus sampling, which focuses the sampling on the most likely tokens.
- Repetition Penalty: A penalty applied to tokens that have already been generated, to discourage repetition.
- Max New Tokens: The maximum number of new tokens to generate.
- Seed: A random seed value to ensure reproducible results.
Outputs
- The model outputs an array of generated text, which can be concatenated to form a coherent response.
Capabilities
qwen1.5-72b is a powerful language model capable of generating human-like text on a wide range of topics. It can be used for tasks such as text completion, language generation, and dialogue systems. The model's performance can be tuned by adjusting the input parameters, allowing users to generate outputs that are more or less creative, coherent, and diverse.
What can I use it for?
qwen1.5-72b can be used in a variety of applications, such as:
- Chatbots and virtual assistants
- Content generation for websites, blogs, and social media
- Creative writing and story generation
- Language translation and summarization
- Educational and research applications
The model's lightweight and efficient design also makes it suitable for deployment on edge devices, enabling on-device language processing capabilities.
Things to try
One interesting aspect of qwen1.5-72b is its ability to generate diverse and creative outputs by adjusting the temperature parameter. By experimenting with different temperature values, users can explore the model's range of capabilities, from more logical and coherent responses to more imaginative and unpredictable outputs. Additionally, the model's system prompt feature allows users to tailor the model's personality and behavior to suit their specific needs, opening up a wide range of potential applications.
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
qwen1.5-110b
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qwen1.5-110b is a transformer-based decoder-only language model developed by lucataco. It is the beta version of Qwen2, a model pretrained on a large amount of data. qwen1.5-110b shares similarities with other models created by lucataco, such as phi-3-mini-4k-instruct and qwen1.5-72b, which are also transformer-based language models. Model inputs and outputs qwen1.5-110b takes in a text prompt and generates a response. The input prompt can be customized with additional parameters like temperature, top-k, top-p, and repetition penalty to control the model's output. Inputs Prompt**: The input text prompt for the model to generate a response. System Prompt**: An additional prompt that sets the tone and context for the model's response. Temperature**: A value used to modulate the next token probabilities, affecting the creativity and diversity of the output. 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 this value. Max New Tokens**: The maximum number of tokens the model should generate as output. Repetition Penalty**: A value that penalizes the model for repeating the same tokens, encouraging more diverse output. Outputs Text**: The generated response from the model based on the provided input prompt and parameters. Capabilities qwen1.5-110b is a powerful language model capable of generating human-like text on a wide range of topics. It can be used for tasks such as text generation, language understanding, and even creative writing. The model's performance can be fine-tuned by adjusting the input parameters to suit specific use cases. What can I use it for? qwen1.5-110b can be used for a variety of applications, such as chatbots, content creation, and language translation. For example, you could use the model to generate product descriptions, write short stories, or even engage in open-ended conversations. Additionally, the model's capabilities can be further expanded by fine-tuning it on domain-specific data, as demonstrated by similar models created by lucataco. Things to try To get the most out of qwen1.5-110b, you can experiment with different input prompts and parameter settings. Try generating text with varying temperatures to explore the model's creativity, or adjust the top-k and top-p values to control the diversity and coherence of the output. Additionally, consider exploring the model's capabilities in combination with other tools and techniques, such as prompt engineering or fine-tuning on specialized datasets.
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qwen-vl-chat
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qwen-vl-chat is a multimodal language model developed by lucataco, a creator featured on AIModels.fyi. It is trained using alignment techniques to support flexible interaction, such as multi-round question answering, and creative capabilities. qwen-vl-chat is similar to other large language models created by lucataco, including qwen1.5-72b, qwen1.5-110b, llama-2-7b-chat, llama-2-13b-chat, and qwen-14b-chat. Model inputs and outputs qwen-vl-chat takes two inputs: an image and a prompt. The image is used to provide visual context, while the prompt is a question or instruction for the model to respond to. Inputs Image**: The input image, which can be any image format. Prompt**: A question or instruction for the model to respond to. Outputs Output**: The model's response to the input prompt, based on the provided image. Capabilities qwen-vl-chat is a powerful multimodal language model that can engage in flexible, creative interactions. It can answer questions, generate text, and provide insights based on the input image and prompt. The model's alignment training allows it to provide responses that are aligned with the user's intent and the visual context. What can I use it for? qwen-vl-chat can be used for a variety of tasks, such as visual question answering, image captioning, and creative writing. For example, you could use it to describe the contents of an image, answer questions about a scene, or generate a short story inspired by a visual prompt. The model's versatility makes it a valuable tool for a range of applications, from education and entertainment to research and development. Things to try One interesting thing to try with qwen-vl-chat is to use it for multi-round question answering. By providing a series of follow-up questions or prompts, you can engage the model in an interactive dialogue and see how it builds upon its understanding of the visual and textual context. This can reveal the model's reasoning capabilities and its ability to maintain coherence and context over multiple exchanges.
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qwen-14b-chat
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qwen-14b-chat is a Transformer-based large language model developed by nomagick, a researcher at Alibaba Cloud. It is the 14 billion parameter version of the Qwen series of large language models, which also includes qwen-1.8b, qwen-7b, and qwen-72b. Like other models in the Qwen series, qwen-14b-chat was pretrained on a large corpus of web texts, books, and code. qwen-14b-chat is an AI assistant model, meaning it was further trained using alignment techniques like supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to make it better at open-ended dialogue and task-completion. Similar to models like chatglm3-6b and chatglm2-6b, qwen-14b-chat can engage in natural conversations, answer questions, and help with a variety of tasks. The qwen-14b base model was trained on over 3 trillion tokens of multilingual data, giving it broad knowledge and capabilities. The qwen-14b-chat model builds on this to become a versatile AI assistant, able to chat, create content, extract information, summarize, translate, code, solve math problems, and more. It can also use tools, act as an agent, and even function as a code interpreter. Model Inputs and Outputs Inputs Prompt**: The text prompt to be completed by the model. This should be formatted in the "chatml" format used by the Qwen models, which includes special tokens like ` and ` to delineate different conversational turns. Top P**: The top-p sampling parameter, which controls the amount of diversity in the generated text. Max Tokens**: The maximum number of new tokens to generate. Temperature**: The temperature parameter, which controls the randomness of the generated text. Outputs The model outputs a list of strings, where each string represents a continuation of the input prompt. The output is generated in a streaming fashion, so the full response can be observed incrementally. Capabilities qwen-14b-chat can engage in open-ended dialogue, answer questions, and assist with a variety of tasks like content creation, information extraction, summarization, translation, coding, and math problem solving. It also has the ability to use external tools, act as an agent, and function as a code interpreter. In benchmarks, qwen-14b-chat has demonstrated strong performance on tasks like MMLU, C-Eval, GSM8K, HumanEval, and long-context understanding, often outperforming other large language models of comparable size. It has also shown impressive capabilities when it comes to tool usage and code generation. What Can I Use It For? qwen-14b-chat is a versatile AI assistant that can be used for a wide range of applications. Some potential use cases include: AI-powered chatbots and virtual assistants**: Use qwen-14b-chat to build conversational AI agents that can engage in natural dialogue, answer questions, and assist with tasks. Content creation**: Leverage qwen-14b-chat to generate articles, stories, scripts, and other types of written content. Language understanding and translation**: Utilize qwen-14b-chat's multilingual capabilities for tasks like text classification, sentiment analysis, and language translation. Code generation and programming assistance**: Integrate qwen-14b-chat into your development workflow to generate code, explain programming concepts, and debug issues. Research and education**: Use qwen-14b-chat as a tool for exploring language models, testing new AI techniques, and educating students about large language models. Things to Try Some interesting things to try with qwen-14b-chat include: Exploring the model's ability to follow different system prompts**: qwen-14b-chat has been trained on a diverse set of system prompts, allowing it to roleplay, change its language style, and adjust its behavior to suit different tasks. Integrating the model with external tools and APIs**: Take advantage of qwen-14b-chat's strong tool usage capabilities by connecting it to various APIs and services through the ReAct prompting approach. Pushing the model's limits on long-context understanding**: The techniques used to extend the context length of qwen-14b-chat, such as NTK-aware interpolation and LogN attention scaling, make it well-suited for tasks that require processing long passages of text. Experimenting with the quantized versions of the model**: The Int4 and Int8 quantized models of qwen-14b-chat offer improved inference speed and reduced memory usage, while maintaining near-lossless performance.
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⛏️
Qwen1.5-72B
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Qwen1.5-72B is a series of large language models developed by Qwen, ranging in size from 0.5B to 72B parameters. Compared to the previous version of Qwen, key improvements include significant performance gains in chat models, multilingual support, and stable support for 32K context length. The models are based on the Transformer architecture with techniques like SwiGLU activation, attention QKV bias, and a mixture of sliding window and full attention. Qwen1.5-32B, Qwen1.5-72B-Chat, Qwen1.5-7B-Chat, and Qwen1.5-14B-Chat are examples of similar models in this series. Model inputs and outputs The Qwen1.5-72B model is a decoder-only language model that generates text based on input prompts. It has an improved tokenizer that can handle multiple natural languages and code. The model does not support direct text generation, and is instead intended for further post-training approaches like supervised finetuning, reinforcement learning from human feedback, or continued pretraining. Inputs Text prompts for the model to continue or generate content Outputs Continuation of the input text, generating novel text Responses to prompts or queries Capabilities The Qwen1.5-72B model demonstrates strong language understanding and generation capabilities, with significant performance improvements over previous versions in tasks like open-ended dialog. It can be used to generate coherent, contextually relevant text across a wide range of domains. The model also has stable support for long-form content with context lengths up to 32K tokens. What can I use it for? The Qwen1.5-72B model and its variants can be used as a foundation for building various language-based AI applications, such as: Conversational AI assistants Content generation tools for articles, stories, or creative writing Multilingual language models for translation or multilingual applications Finetuning on specialized datasets for domain-specific language tasks Things to try Some interesting things to explore with the Qwen1.5-72B model include: Applying post-training techniques like supervised finetuning, RLHF, or continued pretraining to adapt the model to specific use cases Experimenting with the model's ability to handle long-form content and maintain coherence over extended context Evaluating the model's performance on multilingual tasks and code-switching scenarios Exploring ways to integrate the model's capabilities into real-world applications and services
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