model = GPT2LMHeadModel.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id) model.eval() # prepare the prefix prefix_text =r'DeepMind Company is' input_ids = tokenizer(prefix_text, return_tensors='pt').input_ids # generate the result with contrastive search output = model....
inputs = tokenizer(prompt, return_tensors="pt").to(device) # Generate text output = model.generate( inputs["input_ids"], max_length=100, # Maximum length of the generated text num_return_sequences=1, # Number of sequences to generate no_repeat_ngram_size=2, # No repetition of n-...
为了看看我们如何利用温度来影响生成的文本,让我们通过在generate()函数中设置温度参数,以T=2为例进行采样(我们将在下一节解释top_k参数的含义): output_temp = model.generate(input_ids, max_length=max_length, do_sample=True, temperature=2.0, top_k=0) print(tokenizer.decode(output_temp[0])) 1. ...
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9) print(f"Prompt:\n{sample['response']}\n") print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]...
为了看看我们如何利用温度来影响生成的文本,让我们通过在generate()函数中设置温度参数,以T=2为例进行采样(我们将在下一节解释top_k参数的含义): output_temp = model.generate(input_ids, max_length=max_length, do_sample=True, temperature=2.0, top_k=0) ...
from vllm import LLMprompts = ['Hello, my name is', 'The capital of France is'] # Sample prompts.llm = LLM(model='lmsys/vicuna-7b-v1.3') # Create an LLM.outputs = llm.generate(prompts) # Generate texts from the prompts.
{ model = "codellama:7b", url = "http://localhost:11434", -- llm-ls uses "/api/generate" -- cf https://github.com/ollama/ollama/blob/main/docs/api.md#parameters request_body = { -- Modelfile options for the model you use options = { temperature = 0.2, top_p = 0.95, } ...
output_texts = model.generate( input_ids=input_ids, attention_mask=attention_mask, pad_token_id= tokenizer.eos_token_id, eos_token_id= tokenizer.eos_token_id, max_new_tokens=500, do_sample=False, top_k=30, top_p=0.85, temperature=0.3, repetition_penalty=1.2) ...
llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature":0,"max_length":64})) question ="What NFL team won the Super Bowl in the year Justin Beiber was born?"print(llm_chain.run(question)) ...
I am new to Generative AI and I am trying to create a model which will read the resume and check if it matches the job role. I have implemented the code but when I run it, the result which I am getting is not acurate. I am using models from HugginFace, this is...