license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: outputs
results: []
Merged Model Performance
This repository contains our RAG relevance PEFT adapter model.
RAG Relevance Classification Metrics
Our merged model achieves the following performance on a binary classification task:
precision recall f1-score support
0 0.74 0.77 0.75 100
1 0.76 0.73 0.74 100
accuracy 0.75 200
macro avg 0.75 0.75 0.75 200
weighted avg 0.75 0.75 0.75 200
Model Usage
For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit):
def format_input_classification(query, text):
input = f"""
You are comparing a reference text to a question and trying to determine if the reference text
contains information relevant to answering the question. Here is the data:
[BEGIN DATA]
************
[Question]: {query}
************
[Reference text]: {text}
************
[END DATA]
Compare the Question above to the Reference text. You must determine whether the Reference text
contains information that can answer the Question. Please focus on whether the very specific
question can be answered by the information in the Reference text.
Your response must be single word, either "relevant" or "unrelated",
and should not contain any text or characters aside from that word.
"unrelated" means that the reference text does not contain an answer to the Question.
"relevant" means the reference text contains an answer to the Question."""
return input
text = format_input_classification("What is quanitzation?",
"Quantization is a method to reduce the memory footprint")
messages = [
{"role": "user", "content": text}
]
pipe = pipeline(
"text-generation",
model=base_model,
model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16},
tokenizer=tokenizer,
)
Comparison with Other Models
We compared our merged model's performance on the RAG Eval benchmark against several other state-of-the-art language models:
| Model | Precision | Recall | F1 |
|---|---|---|---|
| Our Merged Model | 0.74 | 0.77 | 0.75 |
| GPT-4 | 0.70 | 0.88 | 0.78 |
| GPT-4 Turbo | 0.68 | 0.91 | 0.78 |
| Gemini Pro | 0.61 | 1.00 | 0.76 |
| GPT-3.5 | 0.42 | 1.00 | 0.59 |
| Palm (Text Bison) | 0.53 | 1.00 | 0.69 |
| [1] Scores from arize/phoenix |
As shown in the table, our merged model achieves a comparable score of 0.75, outperforming several other black box models.
We will continue to improve and fine-tune our merged model to achieve even better performance across various benchmarks and tasks.
Citations: [1] https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/retrieval-rag-relevance