PhelixZhen
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Update README.md
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README.md
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@@ -16,4 +16,47 @@ If you are a native English speaker, you might find these sentences uncomfortabl
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Anyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.
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But we hope this will be the beginning of a new great exploration.
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Anyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.
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But we hope this will be the beginning of a new great exploration.
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(We have released a preview version on February 24, 2024, and you can run it using the following code:
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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import torch
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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tokenizer = AutoTokenizer.from_pretrained('/mnt/n/save/tokenizer')
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model = AutoModelForCausalLM.from_pretrained('/mnt/n/save/modelgen4/checkpoint-37920').to(device)
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tokenizer.pad_token = tokenizer.eos_token
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txt = 'A person with a cold should immediately'
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# greedy search
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gen_conf = GenerationConfig(
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num_beams=1,
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do_sample=True,
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max_length=700,
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no_repeat_ngram_size=6,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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temperature=0.93,
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top_k=36,
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top_p=0.80
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)
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tokend = tokenizer.encode_plus(text=txt)
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input_ids, attention_mask = torch.LongTensor([tokend.input_ids]).to(device), \
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torch.LongTensor([tokend.attention_mask]).to(device)
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outputs = model.generate(
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inputs=input_ids,
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attention_mask=attention_mask,
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generation_config=gen_conf,
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)
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outs = tokenizer.decode(outputs[0].cpu().numpy(), clean_up_tokenization_spaces=True,)
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print(outs)
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```
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