Update README.md

README.md

@@ -10,27 +10,123 @@ model-index:
 - name: outputs
   results: []
 ---
+---
+license: mit
+library_name: peft
+tags:
+- trl
+- sft
+- generated_from_trainer
+base_model: microsoft/Phi-3-mini-4k-instruct
+model-index:
+- name: outputs
+  results: []
+---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# outputs
-
-This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+## Merged Model Performance
+
+This repository contains our hallucination evaluation PEFT adapter model.
+
+### Hallucination Detection Metrics
+
+Our merged model achieves the following performance on a binary classification task for detecting hallucinations in language model outputs:
+
+```
+              precision    recall  f1-score   support
+
+           0       0.90      0.98      0.94       100
+           1       0.98      0.89      0.93       100
+
+    accuracy                           0.94       200
+   macro avg       0.94      0.94      0.93       200
+weighted avg       0.94      0.94      0.93       200
+```
+
+### Model Usage
+For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit):
+
+```python
+def format_input(query, response):
+    """Your query field can be a dialogue or a single query with optional context included"""
+    input = f"""Your job is to evaluate whether a machine learning model has hallucinated or not.
+    A hallucination occurs when the response is coherent but factually incorrect or nonsensical 
+    outputs that are not grounded in the provided context.
+    You are given the following information:
+      ####INFO####
+      [Query]: {query}
+      [Model Response]: {response}
+      ####END INFO####
+      Based on the information provided is the model output a hallucination? Respond with only "yes" or "no"
+      """
+      return input
+
+text = format_input(query='Based on the follwoing
+          <context>Walrus are the largest mammal</context>
+          answer the question
+          <query> What is the best PC?</query>',
+          response='The best PC is the mac')
+
+messages = [
+    {"role": "user", "content": text}
+]
+
+pipe = pipeline(
+    "text-generation",
+    model=base_model,
+    model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16},
+    tokenizer=tokenizer,
+)
+generation_args = {
+      "max_new_tokens": 2,
+      "return_full_text": False,
+      "temperature": 0.01,
+      "do_sample": True,
+  }
+
+output = pipe(messages, **generation_args)
+print(f'Hallucination: {output[0]['generated_text'].strip().lower()}')
+# Hallucination: yes
+```
+
+### Comparison with Other Models
+
+We compared our merged model's performance on the hallucination detection benchmark against several other state-of-the-art language models:
+
+| Model                 | Precision | Recall | F1     |
+|---------------------- |----------:|-------:|-------:|
+| Our Merged Model      | 0.75      | 0.87   | 0.81   |
+| GPT-4                 | 0.93      | 0.72   | 0.82   |
+| GPT-4 Turbo           | 0.97      | 0.70   | 0.81   |
+| Gemini Pro            | 0.89      | 0.53   | 0.67   |
+| GPT-3.5               | 0.89      | 0.65   | 0.75   |
+| GPT-3.5-turbo-instruct| 0.89      | 0.80   | 0.84   |
+| Palm 2 (Text Bison)   | 1.00      | 0.44   | 0.61   |
+| Claude V2             | 0.80      | 0.95   | 0.87   |
+
+As shown in the table, our merged model achieves one of the highest F1 scores of 0.81, outperforming several other state-of-the-art language models on this hallucination detection task.
+
+We will continue to improve and fine-tune our merged model to achieve even better performance across various benchmarks and tasks.
+
+Citations:
+ Scores from arize/phoenix
+
+### Training Data
+
+@misc{HaluEval,
+  author = {Junyi Li and Xiaoxue Cheng and Wayne Xin Zhao and Jian-Yun Nie and Ji-Rong Wen },
+  title = {HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models},
+  year = {2023},
+  journal={arXiv preprint arXiv:2305.11747},
+  url={https://arxiv.org/abs/2305.11747}
+}
+ 
+### Framework versions
 
-## Training procedure
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1
 
 ### Training hyperparameters
 
@@ -46,10 +142,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 10
 - training_steps: 150
 
-### Training results
-
-
-
 ### Framework versions
 
 - PEFT 0.11.1