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from xtuner.utils import DEFAULT_IMAGE_TOKEN
def solo_data_llava_map_fn(example):
messages = example['conversations']
input = ''
conversation = []
while messages and messages[0]['from'] == 'gpt':
# Skip the first one if it is from gpt
messages = messages[1:]
for msg in messages:
# For SOLO data: Image SFT pairs and language data.
if 'from' in msg.keys() or 'role' in msg.keys():
if msg['from'] == 'human' or msg['from'] == 'user' or msg['role'] == 'user':
if DEFAULT_IMAGE_TOKEN in msg['value']:
msg['value'] = msg['value'].replace(DEFAULT_IMAGE_TOKEN,
'').strip()
msg['value'] = DEFAULT_IMAGE_TOKEN + '\n' + msg['value']
msg['value'] = msg['value'].strip()
input += msg['value']
elif msg['from'] == 'gpt' or msg['from'] == 'model' or \
msg['role'] == 'assistant' or msg['from'] == 'assistant' or \
msg['from'] == 'assistnat':
conversation.append({'input': input, 'output': msg['value']})
input = ''
else:
raise NotImplementedError
return {'conversation': conversation} |