Starling-RM-34B
Maintainer: Nexusflow
67
🔎
| 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 Starling-RM-34B is a reward model trained from the Yi-34B-Chat language model. Following the method of training reward models in the instructGPT paper, the last layer of Yi-34B-Chat was removed and replaced with a linear layer that outputs a scalar for any pair of input prompt and response. The reward model was trained on the berkeley-nest/Nectar preference dataset using the K-wise maximum likelihood estimator proposed in this paper. The reward model produces a scalar score indicating how helpful and non-harmful a given response is, with higher scores for more helpful and less harmful responses.
Model inputs and outputs
Inputs
- Prompt: The input text that the model will generate a response for.
- Response: The candidate response that will be scored by the reward model.
Outputs
- Reward score: A scalar value indicating the helpfulness and lack of harm in the given response.
Capabilities
The Starling-RM-34B reward model can be used to evaluate the quality and safety of language model outputs. By scoring responses based on their helpfulness and lack of harm, the reward model can help identify potentially harmful or undesirable outputs. This can be particularly useful in the context of Reinforcement Learning from Human Feedback (RLHF), where the reward model is used to provide feedback to an language model during training.
What can I use it for?
The Starling-RM-34B reward model can be used for a variety of applications, including:
- Evaluating language model outputs: By scoring responses based on their helpfulness and lack of harm, the reward model can be used to assess the quality and safety of outputs from large language models.
- Reinforcement Learning from Human Feedback (RLHF): The reward model can be used as part of an RLHF pipeline to provide feedback to a language model during training, helping to align the model's outputs with human preferences.
- Content moderation: The reward model can be used to identify potentially harmful or undesirable content, which can be useful for content moderation tasks.
Things to try
One interesting aspect of the Starling-RM-34B reward model is that it was trained using a preference dataset based on GPT-4 outputs. This means that the model may be biased towards the types of responses and formatting that GPT-4 tends to produce. Researchers and developers could explore how the model's performance and biases change when used with language models other than GPT-4, or when applied to different types of tasks and domains.
Additionally, the use of the K-wise maximum likelihood estimator for training the reward model is an interesting technical detail that could be explored further. Researchers could investigate how this training approach compares to other methods for training reward models, and whether it offers any unique advantages or challenges.
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
🤿
Starling-RM-7B-alpha
95
Starling-RM-7B-alpha is a reward model developed by the berkeley-nest team. It was trained from the Llama2-7B-Chat model using the method of training reward models described in the instructGPT paper. The model was further trained on the berkeley-nest/Nectar dataset, a preference dataset based on GPT-4 preferences. The Starling-RM-7B-alpha model outputs a scalar reward score for any given prompt and response pair. Responses that are more helpful and less harmful will receive a higher reward score. This reward model is likely biased towards GPT-4's preferences, including longer responses and certain response formats. Similar models developed by the berkeley-nest team include the Starling-RM-34B and Starling-LM-7B-alpha models. The Starling-RM-34B model is trained on the same method but uses the larger Yi-34B-Chat as the base model, while the Starling-LM-7B-alpha is a language model trained using the berkeley-nest/Starling-RM-7B-alpha reward model. Model Inputs and Outputs Inputs Prompt**: A piece of text that the model will evaluate and provide a reward score for. Response**: A candidate response to the provided prompt. Outputs Reward Score**: A scalar value representing the model's assessment of how helpful and harmless the given response is for the prompt. Capabilities The Starling-RM-7B-alpha model is able to assess the helpfulness and harmlessness of text responses based on the training data and methodology used. It can be used to rank and compare different responses to the same prompt, favoring those that are more aligned with the preferences in the training data. The model's performance is benchmarked on datasets like Truthful QA, Chatbot Arena Conversations, and PKU's Safe-RLHF, with the Starling-RM-34B model outperforming the Starling-RM-7B-alpha across all these metrics. What Can I Use It For? The Starling-RM-7B-alpha model can be used as part of a reinforcement learning pipeline to train large language models to be more helpful and less harmful. By providing reward scores for model outputs during training, the model can be optimized to generate responses that are aligned with the preferences in the training data. This type of reward model can also be used to evaluate the outputs of other language models, helping to identify responses that may be problematic or undesirable. The model could potentially be integrated into chatbot or virtual assistant applications to help ensure the system behaves in a way that is beneficial to users. Things to Try One interesting thing to try with the Starling-RM-7B-alpha model is to compare its reward scores for different responses to the same prompt. This could help surface nuances in how the model assesses helpfulness and harmlessness. It would also be worth exploring how the model's performance compares to the larger Starling-RM-34B model, and whether the differences in reward scores align with human assessments. Additionally, it could be insightful to probe the model's biases by crafting prompts or responses that play to the preferences in the berkeley-nest/Nectar dataset, and see how the reward scores are affected. This could shed light on the model's limitations and areas for improvement.
Updated Invalid Date
⛏️
Starling-LM-7B-beta
318
Starling-LM-7B-beta is an open large language model (LLM) developed by the Nexusflow team. It is trained using Reinforcement Learning from AI Feedback (RLAIF) and finetuned from the Openchat-3.5-0106 model, which is based on the Mistral-7B-v0.1 model. The model uses the berkeley-nest/Nectar ranking dataset and the Nexusflow/Starling-RM-34B reward model, along with the Fine-Tuning Language Models from Human Preferences (PPO) policy optimization method. This results in an improved score of 8.12 on the MT Bench evaluation with GPT-4 as the judge, compared to the 7.81 score of the original Openchat-3.5-0106 model. Model inputs and outputs Inputs A conversational prompt following the exact chat template provided for the Openchat-3.5-0106 model. Outputs A natural language response to the input prompt. Capabilities Starling-LM-7B-beta is a capable language model that can engage in open-ended conversations, provide informative responses, and assist with a variety of tasks. It has demonstrated strong performance on benchmarks like MT Bench, outperforming several other prominent language models. What can I use it for? Starling-LM-7B-beta can be used for a wide range of applications, such as: Conversational AI**: The model can be used to power chatbots and virtual assistants that engage in natural conversations. Content generation**: The model can be used to generate written content like articles, stories, or scripts. Question answering**: The model can be used to answer questions on a variety of topics. Task assistance**: The model can be used to help with tasks like summarization, translation, and code generation. Things to try One interesting aspect of Starling-LM-7B-beta is its ability to perform well while maintaining a consistent conversational format. By adhering to the prescribed chat template, the model is able to produce coherent and on-topic responses without deviating from the expected structure. This can be particularly useful in applications where a specific interaction style is required, such as in customer service or educational chatbots.
Updated Invalid Date
📉
Starling-LM-7B-alpha
549
Starling-LM-7B-alpha is a large language model developed by the Berkeley NEST team. It is based on the Openchat 3.5 model, which in turn is based on the Mistral-7B-v0.1 model. The key innovation of Starling-LM-7B-alpha is that it was trained using Reinforcement Learning from AI Feedback (RLAIF), leveraging a new dataset called Nectar and a new reward training and policy tuning pipeline. This allows the model to achieve state-of-the-art performance on the MT Bench benchmark, scoring 8.09 and outperforming every model to date except for OpenAI's GPT-4 and GPT-4 Turbo. Model inputs and outputs Starling-LM-7B-alpha is a text-to-text model, taking natural language inputs and generating text outputs. The model uses the same chat template as the Openchat 3.5 model, with the input formatted as Human: {input}\n\nAssistant: and the output being the generated text. Inputs Natural language prompts**: The model can accept a wide variety of natural language prompts, from open-ended questions to task-oriented instructions. Outputs Generated text**: The model outputs generated text that is relevant to the input prompt. This can include responses to questions, explanations of concepts, and task completions. Capabilities Starling-LM-7B-alpha demonstrates strong performance on a variety of benchmarks, including MT Bench, AlpacaEval, and MMLU. It outperforms many larger models like GPT-3.5-Turbo, Claude-2, and Tulu-2-dpo-70b, showcasing its impressive capabilities. The model is particularly adept at tasks that require language understanding and generation, such as open-ended conversations, question answering, and summarization. What can I use it for? Starling-LM-7B-alpha can be used for a variety of applications that require natural language processing, such as: Chatbots and virtual assistants**: The model's strong performance on conversational tasks makes it well-suited for building chatbots and virtual assistants. Content generation**: The model can be used to generate a wide range of text-based content, from articles and stories to product descriptions and marketing copy. Question answering**: The model's ability to understand and respond to questions makes it useful for building question-answering systems. Things to try One interesting aspect of Starling-LM-7B-alpha is its use of Reinforcement Learning from AI Feedback (RLAIF) during training. This approach allows the model to learn from a dataset of human-generated rankings, which can help it better understand and generate responses that are more aligned with human preferences. Experimenting with different prompts and tasks can help you explore how this training approach affects the model's behavior and outputs.
Updated Invalid Date
⛏️
Nemotron-4-340B-Reward
92
The Nemotron-4-340B-Reward is a multi-dimensional reward model developed by NVIDIA. It is based on the larger Nemotron-4-340B-Base model, which is a 340 billion parameter language model trained on a diverse corpus of English and multilingual text, as well as code. The Nemotron-4-340B-Reward model takes a conversation between a user and an assistant, and rates the assistant's responses across five attributes: helpfulness, correctness, coherence, complexity, and verbosity. It outputs a scalar value for each of these attributes, providing a nuanced evaluation of the response quality. This model can be used as part of a synthetic data generation pipeline to create training data for other language models, or as a standalone reward model for reinforcement learning from AI feedback. The model is compatible with the NVIDIA NeMo Framework, which provides tools for customizing and deploying large language models. Similar models in the Nemotron family include the Nemotron-4-340B-Base and Nemotron-3-8B-Base-4k, which are large language models that can be used as foundations for building custom AI applications. Model Inputs and Outputs Inputs A conversation with multiple turns between a user and an assistant Outputs A scalar value (typically between 0 and 4) for each of the following attributes: Helpfulness: Overall helpfulness of the assistant's response to the prompt Correctness: Inclusion of all pertinent facts without errors Coherence: Consistency and clarity of expression Complexity: Intellectual depth required to write the response Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt Capabilities The Nemotron-4-340B-Reward model can be used to evaluate the quality of assistant responses in a nuanced way, providing insights into different aspects of the response. This can be useful for building AI systems that provide helpful and coherent responses, as well as for generating high-quality synthetic training data for other language models. What Can I Use It For? The Nemotron-4-340B-Reward model can be used in a variety of applications that require evaluating the quality of language model outputs. Some potential use cases include: Synthetic Data Generation**: The model can be used as part of a pipeline to generate high-quality training data for other language models, by providing a reward signal to guide the generation process. Reinforcement Learning from AI Feedback (RLAIF)**: The model can be used as a reward model in RLAIF, where a language model is fine-tuned to optimize for the target attributes (helpfulness, correctness, etc.) as defined by the reward model. Reward-Model-as-a-Judge**: The model can be used to evaluate the outputs of other language models, providing a more nuanced assessment than a simple binary pass/fail. Things to Try One interesting aspect of the Nemotron-4-340B-Reward model is its ability to provide a multi-dimensional evaluation of language model outputs. This can be useful for understanding the strengths and weaknesses of different models, and for identifying areas for improvement. For example, you could use the model to evaluate the responses of different language models on a set of prompts, and compare the scores across the different attributes. This could reveal that a model is good at producing coherent and helpful responses, but struggles with providing factually correct information. Armed with this insight, you could then focus on improving the model's knowledge base or fact-checking capabilities. Additionally, you could experiment with using the Nemotron-4-340B-Reward model as part of a reinforcement learning pipeline, where the model's output is used as a reward signal to fine-tune a language model. This could potentially lead to models that are better aligned with human preferences and priorities, as defined by the reward model's attributes.
Updated Invalid Date