UltraLM-13b
Maintainer: openbmb
70
🔄
| Property | Value |
|---|---|
| Model Link | View 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
The UltraLM-13b model is a chat language model fine-tuned from the LLaMA-13b model on the UltraChat dataset. It is maintained by openbmb. Similar models include the 34b-beta model, which is a 34B parameter CausalLM model, and the Llama-2-13b-chat-german model, which is a variant of the Llama 2 13b Chat model fine-tuned on German language data.
Model inputs and outputs
The UltraLM-13b model is a text-to-text model, meaning it takes text as input and generates text as output. The input follows a multi-turn chat format, with the user providing instructions or prompts, and the model generating responses.
Inputs
- User instructions or prompts, formatted as a multi-turn chat
Outputs
- Model responses to the user's prompts, also formatted as a multi-turn chat
Capabilities
The UltraLM-13b model is capable of engaging in open-ended dialogue and task-oriented conversations. It can understand and respond to user prompts on a wide range of topics, drawing upon its extensive training data. The model is particularly adept at tasks like question answering, summarization, and language generation.
What can I use it for?
The UltraLM-13b model can be used for a variety of applications, such as building chatbots, virtual assistants, or interactive language models. It could be integrated into customer service platforms, educational tools, or creative writing applications. Additionally, the model's capabilities could be leveraged for research purposes, such as exploring the limits of language understanding and generation.
Things to try
One interesting thing to try with the UltraLM-13b model is exploring its multi-turn chat capabilities. Provide the model with a series of related prompts and see how it maintains context and continuity in its responses. You could also experiment with prompting the model to engage in specific tasks, such as summarizing long passages of text or answering follow-up questions. Lastly, consider comparing the model's performance to similar language models, such as the 34b-beta or Llama-2-13b-chat-german models, to gain insights into its unique strengths and limitations.
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
🎲
UltraRM-13b
50
The UltraRM-13b model is a reward model developed by the maintainer openbmb and released on the Hugging Face platform. It is trained on the UltraFeedback dataset along with a mixture of other open-source datasets like Anthropic HH-RLHF, Standford SHP, and Summarization. The model is initialized from the LLaMA-13B model and fine-tuned to serve as a reward model for alignment research. Similar models include UltraLM-13b, a chat language model trained on the UltraChat dataset, and Xwin-LM-13B-V0.1, a powerful, stable, and reproducible LLM alignment model built upon the Llama2 base. Model inputs and outputs Inputs input_ids**: A tensor of token IDs representing the input text. attention_mask**: An optional tensor indicating which tokens should be attended to. position_ids**: An optional tensor of position IDs for the input tokens. past_key_values**: An optional list of cached past key-value states for efficient generation. inputs_embeds**: An optional tensor of input embeddings. labels**: An optional tensor of target token IDs for training. Outputs loss**: The computed loss value (only returned during training). logits**: The output logits tensor. past_key_values**: The past key-value states for efficient generation. hidden_states**: An optional tuple of the model's output hidden states. attentions**: An optional tuple of the model's attention weights. Capabilities The UltraRM-13b model is a powerful reward model that can be used to facilitate alignment research for large language models. It has been shown to achieve state-of-the-art performance on several public preference test sets, outperforming other open-source reward models. The model's strong performance is attributed to its fine-tuning on a mixture of datasets, including the custom UltraFeedback dataset. What can I use it for? The UltraRM-13b model can be used as a reward model for alignment research, helping to train and evaluate large language models to be more reliable, safe, and aligned with human values. Researchers and developers working on improving the safety and reliability of AI systems can use this model to provide rewards and feedback during the training process, helping to steer the model's behavior in a more desirable direction. Things to try Researchers can explore fine-tuning the UltraRM-13b model on additional datasets or using it in combination with other alignment techniques, such as inverse reinforcement learning or reward modeling. Developers can also experiment with using the UltraRM-13b model to provide feedback and rewards to their own language models, potentially improving the models' safety and reliability.
Updated Invalid Date
🔮
34b-beta
56
The 34b-beta model is a large language model created by CausalLM. It is a 34 billion parameter model that is designed for text-to-text generation tasks. The model builds on the capabilities of other large language models like the CausalLM 7B and CausalLM 14B versions, which have demonstrated strong performance on a variety of benchmarks. Model inputs and outputs Inputs The model accepts natural language prompts in the chatml format. The model can take prompts of varying lengths, though there are some precision issues with longer sequences that will be addressed in future updates. Outputs The model generates human-like text continuations of the provided prompts. The outputs can be used for a wide range of text-to-text generation tasks, such as content creation, question answering, and dialogue. Capabilities The 34b-beta model has shown strong performance on a variety of benchmarks, including MMLU where it achieved an average accuracy of 63.82%, outperforming many smaller models. It has also performed well on the CEval and GSM8K benchmarks. Additionally, the model has demonstrated a high win rate of 88.26% on the AlpacaEval leaderboard, suggesting it has strong conversational and task-completion abilities. What can I use it for? The 34b-beta model can be used for a wide range of text-to-text generation tasks, such as content creation, question answering, dialogue, and more. Given its strong performance on benchmarks, it could be a valuable tool for companies or individuals working on language-based applications or services. However, it's important to note that the model was trained on unfiltered internet data, so users will need to carefully monitor the outputs for any objectionable content. Things to try One interesting aspect of the 34b-beta model is its potential for multimodal capabilities. The model was fine-tuned on the prompt format introduced in LLaVA1.5, which is unrelated to image attention calculation. This suggests that the model may be able to effectively integrate visual information, opening up possibilities for tasks like image captioning or visual question answering. Users interested in exploring these capabilities should consider aligning the ViT Projection module with the frozen language model. Additionally, the model's strong performance on the MMLU and CEval benchmarks indicates that it could be a useful tool for knowledge-intensive tasks, such as question answering or fact-checking. Users may want to experiment with prompts that leverage the model's broad base of knowledge.
Updated Invalid Date
🔎
llama2-70b-oasst-sft-v10
73
The llama2-70b-oasst-sft-v10 model is a fine-tuned version of Meta's Llama2 70B LLM developed by the Open-Assistant team. It was first fine-tuned on a mix of synthetic instructions and coding tasks, and then further refined on the best human demonstrations collected through the open-assistant.io platform up to July 23, 2023. This model aims to provide an engaging and helpful AI assistant. Similar models include the codellama-13b-oasst-sft-v10 which is a fine-tuning of Meta's CodeLlama 13B LLM, the llama2-13b-orca-8k-3319 which is a fine-tuning of the Llama2 13B model for long-form dialogue, and the stablelm-7b-sft-v7-epoch-3 which is a supervised fine-tuning of the StableLM 7B model. Model inputs and outputs Inputs Text prompts**: The model takes in text prompts that can include multiple turns of conversation between a user and an assistant, formatted using the OpenAI chatml standard. Outputs Continued conversation**: The model generates continued responses to the provided prompts, in the style of an engaging and helpful AI assistant. Capabilities The llama2-70b-oasst-sft-v10 model has been fine-tuned to engage in open-ended dialogue, answering questions, and assisting with a variety of tasks. It demonstrates strong performance on benchmarks for commonsense reasoning, world knowledge, and reading comprehension compared to other large language models. The model also exhibits improved safety and truthfulness compared to earlier versions, making it suitable for use cases requiring reliable and trustworthy responses. What can I use it for? The llama2-70b-oasst-sft-v10 model can be used to build engaging AI assistants for a variety of applications, such as customer support, task planning, research assistance, and creative ideation. Its broad knowledge and language understanding capabilities make it well-suited for open-ended conversations and complex question-answering. Developers can fine-tune or adapt the model further for specific use cases, leveraging the Hugging Face Transformers library and the Open-Assistant resources to integrate the model into their applications. Things to try One interesting aspect of the llama2-70b-oasst-sft-v10 model is its ability to engage in multi-turn conversations, maintaining context and continuity throughout the dialogue. Developers can experiment with prompting the model with longer conversation threads, observing how it maintains the flow of the discussion and provides relevant and coherent responses. Another aspect to explore is the model's safety and truthfulness features, which have been improved through the fine-tuning process. Developers can assess the model's outputs for potential biases, hallucinations, or unsafe content, and further fine-tune or prompt the model to ensure it behaves in an ethical and trustworthy manner for their specific use cases.
Updated Invalid Date
🐍
pygmalion-13b
109
pygmalion-13b is a conversational language model based on Meta's LLaMA-13B. It has been fine-tuned using a subset of the data from the Pygmalion-6B-v8-pt4 project. This model is version 1 and is part of the Pygmalion line of dialogue models from PygmalionAI. The pygmalion-7b model is a similar conversational model based on the LLaMA-7B, also fine-tuned using Pygmalion-6B-v8-pt4 data. Both models follow the usual Pygmalion persona and chat format. Model inputs and outputs Inputs Text prompt**: The model expects input text following a specific format, including the character's persona and dialogue history. Outputs Generated text**: The model outputs generated text responses continuing the dialogue. Capabilities The pygmalion-13b model is designed for open-ended conversational tasks. It is capable of engaging in back-and-forth dialogues, taking on different personas, and generating relevant and coherent responses. The model can discuss a wide range of topics, drawing upon its broad knowledge base. What can I use it for? The pygmalion-13b model can be used for various interactive applications, such as chatbots, virtual assistants, or creative writing tools. Its conversational abilities make it well-suited for applications where natural language interaction is important, like customer service, education, or interactive entertainment. Things to try One interesting aspect of the pygmalion-13b model is its ability to adapt to different personas and styles of interaction. You could experiment with providing varied persona descriptions to see how the model responds and develops the character over the course of a conversation. Additionally, trying out different prompting strategies, such as including more or less dialogue history, can yield interesting results and insights about the model's capabilities.
Updated Invalid Date