--- license: mit datasets: - Open-Orca/SlimOrca-Dedup - migtissera/Synthia-v1.3 - LDJnr/Verified-Camel - LDJnr/Pure-Dove - LDJnr/Capybara - meta-math/MetaMathQA - Intel/orca_dpo_pairs - argilla/ultrafeedback-binarized-preferences-cleaned --- ![Phi-2 Orange](https://huggingface.co/rhysjones/phi-2-orange/resolve/main/phi-2-orange.jpg) # Phi-2 Orange A two-step finetune of Phi-2, with a bit of zest. There is an updated model at [rhysjones/phi-2-orange-v2](https://huggingface.co/rhysjones/phi-2-orange-v2) which has higher evals, if you wish to test. # Training details A first finetune using a collection of broad training data: - [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup) - [migtissera/Synthia-v1.3](https://huggingface.co/datasets/migtissera/Synthia-v1.3) - [LDJnr/Verified-Camel](https://huggingface.co/datasets/LDJnr/Verified-Camel) - [LDJnr/Pure-Dove](https://huggingface.co/datasets/LDJnr/Pure-Dove) - [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) - [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) And then a DPO finetune using: - [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) - [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) # Run within Ollama If you're using [Ollama](https://ollama.ai), you can download and run using: ``` ollama run rhysjones/phi-2-orange ``` # Prompt Format Phi-2 Orange uses ChatML as the prompt format, with or without the system instruction. To prompt with a system instruction (use whatever system prompt you like): ``` <|im_start|>system You are a helpful assistant for Python which outputs in Markdown format.<|im_end|> <|im_start|>user Write a function to calculate the Fibonacci sequence<|im_end|> <|im_start|>assistant ``` You can also omit the system prompt if you wish: ``` <|im_start|>user Why is the sky blue?<|im_end|> <|im_start|>assistant ``` # Evaluations Evaluations done using mlabonne's usefull [Colab notebook llm-autoeval](https://github.com/mlabonne/llm-autoeval). Also check out the alternative leaderboard at [Yet_Another_LLM_Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |----------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)| **33.37**| 71.33| 49.87| **37.3**| **47.97**| |[phi-2-dpo](https://huggingface.co/lxuechen/phi-2-dpo)| 30.39| **71.68**| **50.75**| 34.9| 46.93| |[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)| 33.12| 69.85| 47.39| 37.2| 46.89| |[phi-2](https://huggingface.co/microsoft/phi-2)| 27.98| 70.8| 44.43| 35.21| 44.61|