WillHeld commited on
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04f373e
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1 Parent(s): b362c6e

Update app.py

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Files changed (1) hide show
  1. app.py +2 -3
app.py CHANGED
@@ -13,7 +13,7 @@ def predict(message, history, temperature, top_p):
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  print(history)
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  if len(history) == 0:
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  history.append({"role": "system", "content": """
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- You are a helpful, knowledgeable, and versatile AI assistant powered by Marin 8B Instruct (deeper-starling-05-15). You were built as part of the Marin Project by the Marin Community team.
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  ## CORE CAPABILITIES:
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  - Assist users with a wide range of questions and tasks across domains
@@ -36,8 +36,7 @@ You are a helpful, knowledgeable, and versatile AI assistant powered by Marin 8B
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  - Anyone can contribute to Marin by exploring new architectures, algorithms, datasets, or evaluations
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  - If users ask you to learn more about Marin, point them to https://marin.community
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- Your primary goal is to be a helpful assistant for all types of queries, while having knowledge about the Marin project that you can share when relevant to the conversation.
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- """})
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  history.append({"role": "user", "content": message})
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  input_text = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
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  inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
 
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  print(history)
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  if len(history) == 0:
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  history.append({"role": "system", "content": """
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+ You are a helpful, knowledgeable, and versatile AI assistant powered by Marin 8B Instruct (deeper-starling-05-15), which was trained by the Marin team.
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  ## CORE CAPABILITIES:
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  - Assist users with a wide range of questions and tasks across domains
 
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  - Anyone can contribute to Marin by exploring new architectures, algorithms, datasets, or evaluations
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  - If users ask you to learn more about Marin, point them to https://marin.community
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+ Your primary goal is to be a helpful assistant for all types of queries, while having knowledge about the Marin project that you can share when relevant to the conversation."""})
 
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  history.append({"role": "user", "content": message})
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  input_text = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
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  inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)