Update README.md

README.md

@@ -11,8 +11,9 @@ tags:
 - pytorch
 ---
 
+
 ## Model Details
-We release ChatQA1.5, which excels at RAG-based conversational question answering (QA). ChatQA-1.5 is built using the training recipe from ChatQA (1.0), and it is built on top of Llama-3 foundation model. Additionally, we incorporate more conversational QA data to enhance its tabular and arithmatic calculation capability. ChatQA-1.5 has two variants: ChatQA-1.5-8B and ChatQA-1.5-70B.
+We release ChatQA1.5, which excels at RAG-based conversational question answering (QA). ChatQA-1.5 is built using the training recipe from [ChatQA (1.0)](https://arxiv.org/abs/2401.10225), and it is built on top of Llama-3 foundation model. Additionally, we incorporate more conversational QA data to enhance its tabular and arithmatic calculation capability. ChatQA-1.5 has two variants: ChatQA-1.5-8B and ChatQA-1.5-70B.
 
 
 ## Benchmark Results
@@ -63,7 +64,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
 
 messages = [
-    {"role": "user", "content": "what is the percentage change of the revenue from fourth quarter fiscal year 2023 to fourth quarter fiscal year 2024?"}
+    {"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"}
 ]
 
 context = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n| $ in millions, except earnings per share | Q4 FY24 | Q3 FY24 | Q4 FY23 | Q/Q | Y/Y |\n| Revenue | $22,103 | $18,120 | $6,051 | Up 22% | Up 265% |\n| Gross margin | 76.0% | 74.0% | 63.3% | Up 2.0 pts | Up 12.7 pts |\n| Operating expenses | $3,176 | $2,983 | $2,576 | Up 6% | Up 23% |\n| Operating income | $13,615 | $10,417 | $1,257 | Up 31% | Up 983% |\n| Net income | $12,285 | $9,243 | $1,414 | Up 33% | Up 769% |\n| Diluted earnings per share | $4.93 | $3.71 | $0.57 | Up 33% | Up 765% |"""
@@ -99,7 +100,7 @@ terminators = [
 
 outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
 
-response = outputs[0][input_ids.shape[-1]:]
+response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
 print(tokenizer.decode(response, skip_special_tokens=True))
 ```