from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model from trl.core import respond_to_batch model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2') model_ref = create_reference_model(model) tokenizer = AutoTokenizer.from_pretrained('gpt2') token...
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model from trl.core import respond_to_batch # 首先加载模型,然后创建参考模型 model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2') model_ref = create_reference_model(model) tokenizer = AutoTokenize...
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model from trl.core import respond_to_batch# 首先加载模型,然后创建参考模型model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2') model_ref = create_reference_model(model) tokenizer = AutoTokenizer....
Scores from the reward model, shape (`batch_size`) logprobs (`torch.FloatTensor`): Log probabilities of the model, shape (`batch_size`, `response_length`) ref_logprobs (`torch.FloatTensor`): Log probabilities of the reference model, shape (`batch_size`, `response_length`) """cnt=0r...
74 - # If ZeRO-3 is used, we shard both the active and reference model. 75 - # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0) 76 - if config_kwargs["zero_optimization"]["stage"] != 3: 77 - config_kwarg...
The evaluation could be a human in the loop or another model's output.# imports import torch from transformers import AutoTokenizer from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model from trl.core import respond_to_batch # get models model = Auto...
- co-create with key experts and stakeholders to examine the prior knowledge base and use it to produce the prototype and to identify weaknesses and risks.- refining the proof of concept and the components to develop the preliminary prototype. 4 Prototype A working model or preliminary version ...
model_ref = create_reference_model(model) tokenizer = AutoTokenizer.from_pretrained('gpt2') # initialize trainer ppo_config = PPOConfig( batch_size=1, ) # encode a query query_txt = "This morning I went to the " query_tensor = tokenizer.encode(query_txt, return_tensors="pt") ...
Plasma gasification is a process in which plasma torches create electric arc via passage of electric current through a gas. The process temperature is very high, up to 15,000 °C, and can be controlled independently from fluctuations in feedstock properties and quality. Plasma gasifiers have high...
在这一节, 我们需要先完成SFT模型以及 reward model 的训练, 这部分我们使用LMFlow 完成。 2.1 SFT 这是数据集/home/usrname/LMFlow/data/hh_rlhf/sft/hh_rlhf_sft.json的一个示例。我们只使用首选回应,因此我们得到 112K 个训练样本。 {"type": "text_only", "instances": [{"text": "###Human:...