starling-lm-7b-alpha
Maintainer: tomasmcm
44
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | View on Arxiv |
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Model overview
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.
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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