pip install -i https://pypi.tuna./simple transformers_stream_generator transformers_stream_generator的使用方法 1、基础用法 # 只需在您的原始代码之前添加两行代码 from transformers_stream_generator import init_stream_support init_stream_support() #在model.generate函数中添加do_stream=True,保持do_sample=...
File "/home/coolpadadmin/work/coolai_test/llm/llm_glm3-6b/ChatGLM3/openai_api_demo/utils.py", line 81, in generate_stream_chatglm3 for total_ids in model.stream_generate(**inputs, eos_token_id=eos_token_id, **gen_kwargs): File "/home/coolpadadmin/.local/lib/python3.12/site-pack...
Base class from which `.generate()` streamers should inherit. """defput(self, value):"""Function that is called by `.generate()` to push new tokens"""# 抛出未实现错误,子类需要实现该方法raiseNotImplementedError()defend(self):"""Function that is called by `.generate()` to signal the en...
XVERSE-MoE-A4.2B是由深圳元象科技自主研发的支持多语言的大语言模型(Large Language Model),使用混合专家模型(MoE,Mixture-of-experts)架构,模型的总参数规模为 258 亿,实际激活的参数量为 42 亿,本次开源的模型为底座模型XVERSE-MoE-A4.2B,主要特点如下: 模型结构:XVERSE-MoE-A4.2B 为 Decoder-only 的 Tran...
将语言模型头用于模型的生成序列的 tokenID。<提示警告={true}>大多数生成控制参数都设置在 generation_config 中,如果没有传递,则将设置为模型的默认生成配置。您可以通过传递相应的参数来覆盖任何 generation_config,例如.generate(inputs,num_beams=4,do_sample=True)。
generate_kwargs仅在底层模型是生成模型时才传递给底层模型。 返回 一个dict或dict的列表 字典有两个键: audio (np.ndarray,形状为(nb_channels, audio_length))— 生成的音频波形。 sampling_rate (int)— 生成的音频波形的采样率。 从输入生成语音/音频。有关更多信息,请参阅 TextToAudioPipeline 文档。
(url, stream=True).raw) >>> pixel_values = image_processor(image, return_tensors="pt").pixel_values >>> # autoregressively generate caption (uses greedy decoding by default) >>> generated_ids = model.generate(pixel_values) >>> generated_text = tokenizer.batch_decode(generated_ids, skip...
>>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20) An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven, end <来源> ( ) 刷新任何剩余的缓存并将换行符打印到标准输出。 on_finalized_text <来源> ( text: str stream_end: bool...
get(url, stream=True).raw >>> image = Image.open(image_data) # Allocate a pipeline for object detection >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': ...
from PIL import Image import requests url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png" image = Image.open(requests.get(url, stream=True).raw) image 为模型准备图像。 device = "cuda" if torch.cuda.is_available() else "cpu" inputs = processor(images...