mirror of https://github.com/THUDM/ChatGLM2-6B
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModel, AutoModelForCausalLM
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from transformers import pipeline
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checkpoint = "bigscience/mt0-large"
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checkpoint = "/Users/hhwang/models/gpt2"
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checkpoint = "/Users/hhwang/models/opt-125m"
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checkpoint = "/Users/hhwang/models/opt-350m"
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# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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# model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
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# inputs = tokenizer.encode("Write a short story", return_tensors="pt")
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# outputs = model.generate(inputs)
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# print(tokenizer.decode(outputs[0]))
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# case 1
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# pipe = pipeline(task='text-generation', model=checkpoint)
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# print(pipe)
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# result = pipe("tell me a joke")
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# print('result: ',result)
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# case 2
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# from transformers import GPT2Tokenizer, GPT2LMHeadModel
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# tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
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# model = GPT2LMHeadModel.from_pretrained(checkpoint)
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# text = "Replace me by any text you'd like."
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# encoded_input = tokenizer.encode(text, return_tensors='pt')
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# outputs = model.generate(encoded_input, max_length=50, num_return_sequences=1)
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# generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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# for i, generated_text in enumerate(generated_texts):
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# print(f"Generated text {i + 1}: {generated_text}")
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# # case 3
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# from transformers import GPT2Tokenizer, GPT2Model
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# tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
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# model = GPT2Model.from_pretrained(checkpoint)
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# text = "Replace me by any text you'd like."
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# encoded_input = tokenizer(text, return_tensors='pt')
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# outputs = model(**encoded_input)
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# print(outputs)
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# last_hidden_states = outputs.last_hidden_state
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# print(last_hidden_states)
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# case 4
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# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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# model = AutoModel.from_pretrained(checkpoint)
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# inputs = tokenizer.encode("Write a short story", return_tensors="pt")
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# model = model.eval()
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# print(inputs)
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# outputs = model(inputs)
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# print(outputs)
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# case 5
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print('********* case 5 ***********')
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForCausalLM.from_pretrained(checkpoint)
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inputs = tokenizer.encode("Write a short story", return_tensors="pt")
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outputs = model.generate(inputs)
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print('result: ',tokenizer.batch_decode(outputs))
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# case 6
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print('********* case 6 ***********')
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from transformers import GPT2Tokenizer, OPTForCausalLM
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model = OPTForCausalLM.from_pretrained(checkpoint)
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tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
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prompt = "Anti Vaccine Movemenet"
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inputs = tokenizer(prompt, return_tensors="pt").input_ids
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gen_tokens = model.generate(inputs,do_sample=True,temperature=0.9,max_length=100)
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gen_text = tokenizer.batch_decode(gen_tokens)[0]
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print('gen_text', gen_text)
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# generate_ids = model.generate(inputs,max_length=2000,early_stopping= True,do_sample=True,min_length=2000,top_k=125,top_p=0.92,temperature= 0.85,repetition_penalty=1.5,num_return_sequences=3)
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# for i, sample_output in enumerate(generate_ids):
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# result = tokenizer.decode(sample_output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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# print(result)
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