RLXF
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the best collection of RLXF model including RLHF, RLAIF etc. • 3 items • Updated • 1
dpo-phi2 is an instruction-tuned model from microsoft/phi-2. Direct preference optimization (DPO) is used for fine-tuning on argilla/distilabel-intel-orca-dpo-pairs dataset.
Limitations of dpo-phi2
Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring trainig data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 61.26 |
| AI2 Reasoning Challenge (25-Shot) | 61.69 |
| HellaSwag (10-Shot) | 75.13 |
| MMLU (5-Shot) | 58.10 |
| TruthfulQA (0-shot) | 43.99 |
| Winogrande (5-shot) | 74.19 |
| GSM8k (5-shot) | 54.44 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard61.690
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard75.130
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard58.100
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard43.990
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard74.190
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard54.440