Phi-2 Orange Version 2
A two-step finetune of Phi-2, with a bit more zest.
This is an improved version of the original Phi-2-Orange that uses an updated training process on the same datasets.
It also uses the latest updated model from Microsoft's Phi-2, making it directly usable within Hugging Face's Transformers library (without the need for trust remote code).
Prompt Format
Phi-2 Orange v2 uses ChatML as the prompt format.
(Update 12th March 2024: fixed eos_token issue)
It's recommended to always 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
For example, if you find the model's output to be overly verbose, instruct it to be short and concise:
<|im_start|>system
You are a helpful assistant. Be short and direct in your answers.<|im_end|>
<|im_start|>user
Was Tom Hanks in the movie Forrest Gump? If so, who did he play and give details of the plot.<|im_end|>
<|im_start|>assistant
Evaluations
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Average | 63.67 |
AI2 Reasoning Challenge (25-Shot) | 61.86 |
HellaSwag (10-Shot) | 76.32 |
MMLU (5-Shot) | 55.72 |
TruthfulQA (0-shot) | 54.84 |
Winogrande (5-shot) | 75.69 |
GSM8k (5-shot) | 57.62 |
YALL - Yet Another LLM Leaderboard
Evaluation from mlabonne's alternative LLM leaderboard:
Metric | Value |
---|---|
Average | 49.64 |
AGIEval | 34.55 |
GPT4All | 70.96 |
TruthfulQA | 54.87 |
Bigbench | 38.17 |
Limitations
This model shares the same limitations as the underlying Phi-2 model, details of which are found here.
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard61.860
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard76.320
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard55.720
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard54.840
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard75.690
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard57.620