Starling-LM-7B-beta
Maintainer: Nexusflow
318
⛏️
| 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-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.
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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