Starling-LM-7B-alpha
Maintainer: berkeley-nest
549
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| 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 |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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starling-lm-7b-alpha
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The starling-lm-7b-alpha is an open large language model (LLM) developed by berkeley-nest and trained using Reinforcement Learning from AI Feedback (RLAIF). The model is built upon the Openchat 3.5 base model and uses the berkeley-nest/Starling-RM-7B-alpha reward model and the advantage-induced policy alignment (APA) policy optimization method. The starling-lm-7b-alpha model scores 8.09 on the MT Bench benchmark, outperforming many other LLMs except for OpenAI's GPT-4 and GPT-4 Turbo. Similar models include the Starling-LM-7B-beta which uses an upgraded reward model and policy optimization technique, as well as stable-diffusion and stablelm-tuned-alpha-7b from Stability AI. Model inputs and outputs Inputs prompt**: The text prompt to send to the model. max_tokens**: The maximum number of tokens to generate per output sequence. temperature**: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random. top_k**: An integer that controls the number of top tokens to consider during generation. top_p**: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1. presence_penalty**: A float that penalizes new tokens based on whether they appear in the generated text so far, with values greater than 0 encouraging the use of new tokens and values less than 0 encouraging token repetition. frequency_penalty**: A float that penalizes new tokens based on their frequency in the generated text so far, with values greater than 0 encouraging the use of new tokens and values less than 0 encouraging token repetition. stop**: A list of strings that, when generated, will stop the generation process. Outputs Output**: A string containing the generated text. Capabilities The starling-lm-7b-alpha model is capable of generating high-quality text on a wide range of topics, outperforming many other LLMs on benchmark tasks. It can be used for tasks such as language translation, question answering, and creative writing, among others. What can I use it for? The starling-lm-7b-alpha model can be used for a variety of natural language processing tasks, such as: Content Generation**: The model can be used to generate high-quality text for articles, stories, or other types of content. Language Translation**: The model can be fine-tuned for language translation tasks, allowing it to translate text between different languages. Question Answering**: The model can be used to answer a wide range of questions on various topics. Chatbots and Conversational AI**: The model can be used to build conversational AI applications, such as virtual assistants or chatbots. The model is hosted on the LMSYS Chatbot Arena platform, allowing users to test and experiment with the model for free. Things to try One interesting aspect of the starling-lm-7b-alpha model is its ability to generate text with a high degree of coherence and consistency. By adjusting the temperature and other generation parameters, users can experiment with the model's creativity and expressiveness, while still maintaining a clear and logical narrative flow. Additionally, the model's strong performance on benchmark tasks suggests it could be a valuable tool for a wide range of natural language processing applications. Users may want to explore fine-tuning the model for specific domains or tasks, or integrating it into larger AI systems to leverage its capabilities.
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Starling-RM-34B
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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.
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Starling-RM-7B-alpha
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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.
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