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Update README.md
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README.md
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import torch
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import transformers
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from transformers import AutoTokenizer, AutoModelForCausalLM
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#
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assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
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# init
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tokenizer = AutoTokenizer.from_pretrained("snorkelai/RedPajama-7B-Chat-Curated")
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model = AutoModelForCausalLM.from_pretrained("snorkelai/RedPajama-7B-Chat-Curated", torch_dtype=torch.float16)
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model = model.to('cuda:0')
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inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
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input_length = inputs.input_ids.shape[1]
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outputs = model.generate(
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**inputs, max_new_tokens=
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token = outputs.sequences[0, input_length:]
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output_str = tokenizer.decode(token)
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print(output_str)
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"""
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"""
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```
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import torch
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import transformers
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from transformers import AutoTokenizer, AutoModelForCausalLM
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```
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Example with RedPajama-7B-Chat-Curated
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```python
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# Example 1 using RedPajama-7B-Chat-Curated
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tokenizer = AutoTokenizer.from_pretrained("snorkelai/RedPajama-7B-Chat-Curated")
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model = AutoModelForCausalLM.from_pretrained("snorkelai/RedPajama-7B-Chat-Curated", torch_dtype=torch.float16)
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model = model.to('cuda:0')
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## inference
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prompt = "<human>: {EDIT_NEEDED}.\n<bot>:"
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inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
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input_length = inputs.input_ids.shape[1]
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outputs = model.generate(
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**inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.7, top_k=50,
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)
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token = outputs.sequences[0, input_length:]
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output_str = tokenizer.decode(token)
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print(output_str)
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"""{EDIT_NEEDED}"""
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```
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Comparing with RedPajama-INCITE-7B-Chat
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```python
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# Example 1 using RedPajama-INCITE-7B-Chat
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Chat")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Chat", torch_dtype=torch.float16)
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model = model.to('cuda:0')
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## inference
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prompt = "<human>: {EDIT_NEEDED}.\n<bot>:"
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inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
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input_length = inputs.input_ids.shape[1]
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outputs = model.generate(
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**inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.7, top_k=50,
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)
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token = outputs.sequences[0, input_length:]
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output_str = tokenizer.decode(token)
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print(output_str)
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"""{EDIT_NEEDED}"""
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"""
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"""
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```
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