SFR-Iterative-DPO-LLaMA-3-8B-R
Maintainer: Salesforce
70
💬
| 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
The SFR-Iterative-DPO-LLaMA-3-8B-R is a state-of-the-art instruct model developed by Salesforce. It outperforms many open-sourced models as well as strong proprietary models on instruct model benchmarks like Alpaca-Eval-V2, MT-Bench, and Chat-Arena-Hard. The model is trained on open-sourced datasets without any additional human- or GPT4-labeling.
Model inputs and outputs
Inputs
- Text: The model takes text input only.
Outputs
- Text and Code: The model generates text and code.
Capabilities
The SFR-Iterative-DPO-LLaMA-3-8B-R is a highly capable instruct model that can handle a wide variety of tasks. It demonstrates strong performance on general language understanding, knowledge reasoning, and reading comprehension benchmarks. The model also excels at more complex tasks that require following instructions, as evidenced by its superior results on benchmarks like GSM-8K and MATH.
What can I use it for?
The SFR-Iterative-DPO-LLaMA-3-8B-R model can be used for a variety of applications, such as building chatbots, virtual assistants, and other language-based AI systems. Its strong performance on instruction-following tasks makes it particularly well-suited for use cases that require the model to engage in helpful and informative dialogues with users. Developers can leverage the model's capabilities to create applications that assist with tasks like research, analysis, and problem-solving.
Things to try
One interesting aspect of the SFR-Iterative-DPO-LLaMA-3-8B-R model is its use of an online RLHF recipe for instruct training, which is more efficient and simpler to train than the widely-used PPO-based approaches. This innovative training method allows the model to better align with human preferences for helpfulness and safety, making it a valuable tool for developers who prioritize these qualities in their 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
💬
LLaMA-3-8B-SFR-Iterative-DPO-R
73
LLaMA-3-8B-SFR-Iterative-DPO-R is a state-of-the-art instruct model developed by Salesforce. It outperforms similar-sized models, most large open-sourced models, and strong proprietary models on three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, and Chat-Arena-Hard. The model is trained on open-sourced datasets without additional human or GPT4 labeling. The SFR-Iterative-DPO-LLaMA-3-8B-R model follows a similar approach, also outperforming other models on these benchmarks. Salesforce has developed an efficient online RLHF recipe for LLM instruct training, using a DPO-based method that is cheaper and simpler to train than PPO-based approaches. Model Inputs and Outputs Inputs Text prompts Outputs Generated text responses Capabilities The LLaMA-3-8B-SFR-Iterative-DPO-R model has shown strong performance on a variety of instruct model benchmarks. It can engage in open-ended conversations, answer questions, and complete tasks across a wide range of domains. What Can I Use It For? The LLaMA-3-8B-SFR-Iterative-DPO-R model can be used for building conversational AI assistants, automating text-based workflows, and generating content. Potential use cases include customer service, technical support, content creation, and task completion. As with any large language model, developers should carefully consider safety and ethical implications when deploying the model. Things to Try Try prompting the model with specific tasks or open-ended questions to see its versatility and capabilities. You can also experiment with different generation parameters, such as temperature and top-p, to control the model's output. Additionally, consider fine-tuning the model on your own data to adapt it to your specific use case.
Updated Invalid Date
🤿
xLAM-v0.1-r
47
The xLAM-v0.1-r model is a large action model developed by Salesforce. It is an upgraded version of the Mixtral model, with significant improvements in many areas. The xLAM-v0.1-r model has been fine-tuned across a wide range of agent tasks and scenarios, while preserving the capabilities of the original Mixtral model. It is designed to enhance decision-making and translate user intentions into executable actions that interact with the world. Model inputs and outputs The xLAM-v0.1-r model is a text-to-text transformer model that can take in natural language prompts and generate corresponding responses. Inputs Natural language prompts describing tasks or queries Outputs Natural language responses that represent the model's interpretation and execution of the input prompt Capabilities The xLAM-v0.1-r model exhibits strong function-calling capabilities, allowing it to understand natural language instructions and execute corresponding API calls. This enables the model to interact with a variety of digital services and applications, such as retrieving weather information, managing social media platforms, and handling financial services. What can I use it for? The xLAM-v0.1-r model can be leveraged for a wide range of applications that require AI agents to autonomously plan and execute tasks to achieve specific goals. This includes workflow automation, personal assistant services, and task-oriented dialogue systems. The model's ability to translate natural language into structured API calls makes it well-suited for building intelligent software agents that can seamlessly integrate with various digital platforms and services. Things to try One interesting aspect of the xLAM-v0.1-r model is its ability to generate JSON-formatted responses that closely resemble the function-calling mode of ChatGPT. This can be particularly useful for building applications that require structured outputs for easy integration with other systems. Developers can experiment with different prompts and observe how the model translates natural language into executable function calls. Another aspect to explore is the model's performance on the Berkeley Function-Calling Leaderboard (BFCL), where the xLAM-v0.1-r and its smaller counterpart xLAM-1b-fc-r have achieved competitive results. Investigating the model's strengths and weaknesses across the different categories on this benchmark can provide valuable insights for further improving function-calling capabilities.
Updated Invalid Date
👀
FsfairX-LLaMA3-RM-v0.1
46
The FsfairX-LLaMA3-RM-v0.1 model is a reward function developed by sfairXC that can be used for reinforcement learning with human feedback (RLHF), including proximal policy optimization (PPO), iterative supervised fine-tuning (SFT), and iterative discriminator policy optimization (DPO). The model is based on the meta-llama/Meta-Llama-3-8B-Instruct base model and was trained using the script at https://github.com/WeiXiong UST/RLHF-Reward-Modeling. Similar models include SFR-Iterative-DPO-LLaMA-3-8B-R and Llama-3-8B-SFR-Iterative-DPO-R, which are also RLHF-trained models from Salesforce. Model inputs and outputs Inputs Text data, for which the model will provide a sentiment analysis score. Outputs A sentiment analysis score between 0 and 1, where 1 indicates a positive sentiment and 0 indicates a negative sentiment. Capabilities The FsfairX-LLaMA3-RM-v0.1 model can be used as a reward function for RLHF training of large language models. It provides a way to evaluate the safety and alignment of model outputs with human preferences. What can I use it for? The FsfairX-LLaMA3-RM-v0.1 model can be used as part of an RLHF training pipeline for large language models, such as SFR-Iterative-DPO-LLaMA-3-8B-R and Llama-3-8B-SFR-Iterative-DPO-R. By providing a reward signal that aligns with human preferences, the model can help train more helpful and safe language models. Things to try One interesting thing to try with the FsfairX-LLaMA3-RM-v0.1 model is to use it to evaluate the safety and alignment of model outputs during the RLHF training process. By monitoring the reward scores provided by the model, you can gain insights into how the trained model is progressing in terms of safety and alignment with human preferences.
Updated Invalid Date
📊
Llama-3-Instruct-8B-SPPO-Iter3
71
Llama-3-Instruct-8B-SPPO-Iter3 is a large language model developed by UCLA-AGI using the Self-Play Preference Optimization technique. It is based on the Meta-Llama-3-8B-Instruct architecture and was fine-tuned on synthetic datasets from the openbmb/UltraFeedback and snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset datasets. Model Inputs and Outputs Llama-3-Instruct-8B-SPPO-Iter3 is a text-to-text model, meaning it takes in text-based inputs and generates text-based outputs. The model can handle a variety of natural language tasks, including question answering, summarization, and language generation. Inputs Natural language text Instructions or prompts for the model to follow Outputs Generated text responses Answers to questions Summaries of input text Capabilities Llama-3-Instruct-8B-SPPO-Iter3 has demonstrated strong performance on a range of language tasks, as shown by its high scores on the AlpacaEval and Open LLM Leaderboard benchmarks. The model is particularly capable at tasks that require reasoning, inference, and coherent text generation. What Can I Use It For? Llama-3-Instruct-8B-SPPO-Iter3 can be used for a variety of natural language processing applications, such as: Chatbots and virtual assistants Content generation (e.g., articles, stories, scripts) Question answering Summarization Translation The model's strong performance on benchmarks suggests it could be a valuable tool for researchers, developers, and businesses working on language-based AI projects. Things to Try One interesting aspect of Llama-3-Instruct-8B-SPPO-Iter3 is its ability to generate coherent and contextually-appropriate text. You could try giving the model a variety of prompts and observe the diversity and quality of the responses. Additionally, you could experiment with fine-tuning the model on your own datasets to see how it performs on specific tasks or domains.
Updated Invalid Date