from transformers.pipelines.pt_utils import KeyDataset from datasets import load_dataset pipe = pipeline(model="hf-internal-testing/tiny-random-wav2vec2", device=0) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:10]") for out in pipe(KeyDatas...
fromdatasetsimportload_dataset,DatasetDictds_train=load_dataset("huggingface-course/codeparrot-ds-train",split="train")ds_valid=load_dataset("huggingface-course/codeparrot-ds-valid",split="validation")raw_datasets=DatasetDict({"train":ds_train,# .shuffle().select(range(50000)),"valid":ds_valid...
│ 163 │ │ │ model = cls(model, config, adapter_name) │ │ 164 │ │ else: │ │ 165 │ │ │ model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, ad │ │ ❱ 166 │ │ model.load_adapter(model_id, adapter_name, **kwargs) │ │ 167 │ │ ret...
I'm having the same issue, i've fine tuned a Llama 7b model using peft, and got satisfying results in inference, but when i try to use SFTTrainer.save_model, and load the model from the saved files using LlamaForCausalLM.from_pretrained, the inference result seem to just be of the ...
需要编写一个配置文件,随便起个名字,如ollama_Liama3_config.txt 文件放到D盘下的ollama目录中 配置文件内容如下: FROM"D:\ollama\Llama3-8B-Chinese-Chat.q6_k.GGUF"TEMPLATE"""{{- if .System }} <|im_start|>system {{ .System }}<|im_end|> {{- end }} <|im_start|>user {{ .Prompt ...
fromdatasets import load_datasetfromtransformers import AutoTokenizer, pipelinefromtrl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainerfromtqdm import tqdm dataset = load_dataset("HuggingFaceH4/cherry_picked_prompts",split="train")
model.config.distribution_output >>>student_t 这是具体实现层面与用于 NLP 的 Transformers 的一个重要区别,其中头部通常由一个固定的分类分布组成,实现为nn.Linear层。 定义转换 接下来,我们定义数据的转换,尤其是需要基于样本数据集或通用数据集来创建其中的时间特征。
一个完整的transformer模型主要包含三部分:Config、Tokenizer、Model。 Config 用于配置模型的名称、最终输出的样式、隐藏层宽度和深度、激活函数的类别等。 示例: 代码语言:javascript 复制 {"architectures":["BertForMaskedLM"],"attention_probs_dropout_prob":0.1,"gradient_checkpointing":false,"hidden_act":"gel...
fromtransformers import AutoModelForCausalLMfromdatasets import load_datasetfromtrl import SFTTrainer dataset = load_dataset("imdb",split="train") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") peft_config = LoraConfig(r=16,lora_alpha=32,lora_dropout=0.05,bias="none",task_...
from datasets import load_dataset from trl import SFTTrainer dataset = load_dataset("imdb", split="train") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") peft_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, ...