Update README.md

README.md

@@ -68,32 +68,33 @@ The model will output the score as 'PASS' if the answer is faithful to the docum
 To run inference, you can use HF pipeline:
 
 ```
-pipe = pipeline(
-          "text-generation",
-          model="PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct",
-          max_new_tokens=600,
-          device="cuda",
-          return_full_text=False
-        )
+import transformers
+
+model_id = "PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    max_new_tokens=600,
+    device="cuda",
+    eturn_full_text=False
+)
 
 messages = [
     {"role": "user", "content": prompt},
 ]
 
-result = pipe(messages)
-print(result[0]['generated_text'])
+outputs = pipeline(
+    messages,
+    temperature=0
+)
 
+print(outputs[0]["generated_text"])
 ```
 
 Since the model is trained in chat format, ensure that you pass the prompt as a user message.
 
-## Training Details
-
-The model was finetuned for 3 epochs using H100s on dataset of size 2400. We use [lion](https://github.com/lucidrains/lion-pytorch) optimizer with lr=5.0e-7. For more details on data generation, please check out our Github repo.
-
-### Training Data
-
-We train on 2400 samples consisting of CovidQA, PubmedQA, DROP and RAGTruth samples. For datasets that do not contain hallucinated samples, we generate perturbations to introduce hallucinations in the data. For more details about the data generation process, refer to the paper.
+For more information on training details, refer to our [ArXiv paper](https://arxiv.org/abs/2407.08488).
 
 ## Evaluation