Qwen-LLaMAfied-7B-Chat
Maintainer: JosephusCheung
102
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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
The Qwen-LLaMAfied-7B-Chat is a 7B parameter large language model created by JosephusCheung and maintained on the Hugging Face platform. It is a replica of the original Qwen/Qwen-7B-Chat model, but has been recalibrated to fit the LLaMA/LLaMA-2 model structure. The model has been edited to be white-labeled, meaning it no longer refers to itself as a Qwen model. It uses the same tokenizer as the original LLaMA/LLaMA-2 models, and the training process involved numerical alignment of weights and preliminary reinforcement learning to maintain equivalency with the original.
Similar models include the 7B CausalLM model, which is also fully compatible with the Meta LLaMA 2 architecture. This 7B model is said to outperform existing models up to 33B in most quantitative evaluations.
Model inputs and outputs
Inputs
- Text: The model takes text input in the form of a sequence of tokens.
Outputs
- Text: The model generates output text in the form of a sequence of tokens.
Capabilities
The Qwen-LLaMAfied-7B-Chat model has been trained to perform well on a variety of tasks, including commonsense reasoning, code generation, and mathematics. It achieves an average MMLU score of 53.48 and a CEval (val) average of 54.13, which is on par with the original Qwen-7B-Chat model.
What can I use it for?
The Qwen-LLaMAfied-7B-Chat model can be used for a variety of natural language processing tasks, such as text generation, question answering, and language translation. Given its strong performance on benchmarks, it could be a good choice for tasks that require commonsense reasoning or mathematical understanding.
Things to try
One interesting aspect of the Qwen-LLaMAfied-7B-Chat model is its use of the chatml prompt format. Experimenting with different prompt styles and structures could help unlock the model's full potential.
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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