vicuna-33b-v1.3
Maintainer: lmsys
285
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
vicuna-33b-v1.3 is an open-source chatbot developed by the Vicuna team at LMSYS. It is an auto-regressive language model based on the transformer architecture, fine-tuned from the LLaMA model on user-shared conversations collected from ShareGPT. This model builds upon the capabilities of LLaMA with additional training to improve its conversational abilities.
Similar models include the vicuna-13b-v1.5-16K and stable-vicuna-13B-HF, which are also fine-tuned versions of LLaMA with different training data and techniques.
Model inputs and outputs
Inputs
- Text prompts: The model takes text prompts as input, which can be questions, instructions, or conversational starters.
Outputs
- Generated text: The model generates coherent and contextual text responses based on the input prompt. The responses aim to be helpful, detailed, and polite.
Capabilities
vicuna-33b-v1.3 is capable of engaging in open-ended conversations, answering questions, and providing informative responses on a wide range of topics. It demonstrates strong language understanding and generation abilities, with the potential to assist users with tasks such as research, analysis, and creative writing.
What can I use it for?
The primary intended use of vicuna-33b-v1.3 is for research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can use this model to explore advancements in conversational AI. Additionally, the model could be fine-tuned or integrated into various applications that require natural language interactions, such as virtual assistants, customer service chatbots, or educational tools.
Things to try
One interesting aspect of vicuna-33b-v1.3 is its ability to engage in back-and-forth conversations, where it can understand and respond to context. Users can try asking follow-up questions or providing additional context to see how the model adapts its responses. Additionally, users can experiment with different prompting strategies, such as using specific instructions or framing the interaction as a collaborative task, to further explore the model's capabilities.
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
๐งช
vicuna-13b-v1.5
191
vicuna-13b-v1.5 is a large language model developed by LMSYS. It is a 13 billion parameter chat assistant trained by fine-tuning the Llama 2 model on user-shared conversations collected from ShareGPT. The model is licensed under the Llama 2 Community License Agreement. Similar models include the vicuna-7b-v1.5, vicuna-13b-v1.5-16k, vicuna-7b-v1.5-16k, and vicuna-33b-v1.3. Model inputs and outputs vicuna-13b-v1.5 is an autoregressive language model that takes in text as input and generates text as output. It can be used for a variety of natural language processing tasks such as language generation, translation, and question answering. Inputs Text prompts Outputs Generated text responses Capabilities vicuna-13b-v1.5 has been trained to engage in open-ended conversation and provide helpful, informative, and coherent responses on a wide range of topics. It can be used for research on large language models and chatbots, as well as for practical applications like customer service, content creation, and task assistance. What can I use it for? The primary use of vicuna-13b-v1.5 is for research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can use the model to explore topics like conversational AI, language understanding, and knowledge representation. The model can also be used for practical applications like customer service chatbots, content generation, and task assistance. Things to try With vicuna-13b-v1.5, you can experiment with different prompting techniques, such as providing context-specific instructions or engaging the model in multi-turn dialogues. You can also explore the model's capabilities in areas like language generation, question answering, and task completion. The Vicuna Model Card provides more details on the model's sources, training, and evaluation.
Updated Invalid Date
๐๏ธ
vicuna-13b-v1.3
190
The vicuna-13b-v1.3 model is a large language model developed by LMSYS that has been fine-tuned on user-shared conversations collected from ShareGPT. It is an auto-regressive language model based on the transformer architecture, built by fine-tuning the LLaMA model. This model is available in several variants, including vicuna-7b-v1.3, vicuna-13b-v1.1, vicuna-7b-v1.1, and vicuna-33b-v1.3, which differ in their size and training details. Model inputs and outputs The vicuna-13b-v1.3 model is a text-to-text model, taking in natural language text as input and generating natural language text as output. It can be used for a variety of tasks, such as question answering, text generation, and dialogue. Inputs Natural language text prompts Outputs Natural language text responses Capabilities The vicuna-13b-v1.3 model has been trained to engage in open-ended dialogue and assist with a wide range of tasks. It can answer questions, provide explanations, and generate creative content. The model has shown strong performance on various benchmarks and is particularly capable at understanding and responding to user instructions. What can I use it for? The primary use of the vicuna-13b-v1.3 model is for research on large language models and chatbots. The model is intended to be used by researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. Potential use cases include building conversational AI assistants, language generation applications, and language understanding systems. Things to try Researchers and developers can experiment with the vicuna-13b-v1.3 model by integrating it into custom applications through the command line interface or API endpoints provided by the LMSYS team. The model can be used to prototype and test new ideas in the field of conversational AI, exploring its capabilities and limitations.
Updated Invalid Date
โ๏ธ
vicuna-7b-v1.3
123
The vicuna-7b-v1.3 is a chat assistant model developed by LMSYS. It is a fine-tuned version of the LLaMA language model, trained on user-shared conversations collected from ShareGPT. Vicuna aims to be a helpful and polite conversational AI, with capabilities similar to other open-domain chatbots. Compared to the vicuna-33b-v1.3 and vicuna-13b-v1.5-16k models, the vicuna-7b-v1.3 has a smaller parameter size but may have slightly lower performance. Model inputs and outputs Inputs Free-form text prompts for the model to continue or build upon Outputs Coherent and contextual text responses, generated in an auto-regressive manner The model can engage in back-and-forth conversations, remembering previous context Capabilities The vicuna-7b-v1.3 model is capable of engaging in open-ended conversations on a wide range of topics. It can provide informative answers, generate creative stories, and even assist with task planning and brainstorming. However, like other large language models, it may occasionally produce inaccurate or biased information, and its responses can be inconsistent across conversations. What can I use it for? The vicuna-7b-v1.3 model is well-suited for research on conversational AI systems and large language models. Developers and hobbyists in natural language processing, machine learning, and artificial intelligence can use this model to explore areas such as open-domain chatbots, assistants, and language generation. While the model is not intended for commercial use, researchers and enthusiasts may find it useful for experiments, prototyping, and educational purposes. Things to try One interesting aspect of the vicuna-7b-v1.3 model is its ability to maintain context and memory across multiple conversational turns. Try engaging the model in an extended dialogue, asking it to summarize previous points or build upon earlier statements. You can also experiment with different prompting techniques, such as providing the model with specific instructions or persona descriptions, to see how it adjusts its responses.
Updated Invalid Date
๐คท
vicuna-7b-v1.5
240
The vicuna-7b-v1.5 model is a chat assistant developed by LMSYS. It is an auto-regressive language model based on the transformer architecture, fine-tuned from Llama 2 on user-shared conversations collected from ShareGPT. The model aims to be useful for research on large language models and chatbots, with the primary intended users being researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. Similar models include the vicuna-33b-v1.3 which is also a Vicuna model fine-tuned from a larger LLaMA base, and the vicuna-13B-v1.5-16K-GGML which is a GGML version of the 13B Vicuna model, optimized for CPU and GPU inference. Model inputs and outputs Inputs Prompt**: The model takes a free-form text prompt as input, which can be a question, instruction, or conversational message. Outputs Text response**: The model generates a coherent text response based on the input prompt. The response aims to be helpful, detailed, and polite. Capabilities The vicuna-7b-v1.5 model is capable of engaging in open-ended conversations on a wide range of topics. It can answer questions, provide explanations, and offer suggestions based on the input prompt. The model demonstrates strong performance on standard benchmarks, human preference tests, and LLM-as-a-judge evaluations, achieving around 90% of the quality of GPT-4 according to the Vicuna team. What can I use it for? The primary use case for the vicuna-7b-v1.5 model is research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can experiment with the model, explore its capabilities, and use it as a starting point for further fine-tuning or development. Things to try One interesting aspect of the vicuna-7b-v1.5 model is its fine-tuning on user-shared conversations from ShareGPT. This means the model has been exposed to a diverse range of conversational styles and topics, which could allow it to engage in more natural and context-aware dialogue compared to models trained on more curated datasets. Experimenting with open-ended conversations on a variety of subjects could help uncover the model's strengths and limitations in real-world settings.
Updated Invalid Date