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...
您可以通过使用TextToAudioPipeline.__call__.forward_params或TextToAudioPipeline.__call__.generate_kwargs来指定传递给模型的参数。 示例: 代码语言:javascript 复制 >>> from transformers import pipeline >>> music_generator = pipeline(task="text-to-audio", model="facebook/musicgen-small", framework=...
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...
get(url, stream=True).raw) 图像字幕(不提供文本提示): 代码语言:javascript 复制 >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) >>> generated_ids = model.generate(**inputs) >>> generated_text = processor.batch_decode(generated_ids, skip_special_...
stream_chunk_s:Optional[int] =None, stride_length_s:Optional[Union[Tuple[float,float],float]] =None, format_for_conversion:str="f32le",):""" 实时读取麦克风音频数据的辅助函数。 """# 如果 stream_chunk_s 不为 None,则将其作为 chunk_s;否则使用 chunk_length_s 作为 chunk_sifstream_chunk...
pip install modelscope sentencepiece accelerate fastapi uvicorn requests streamlit transformers_stream_generator # 安装flash-attention # 这个也是不行使用 pip install https://github.moeyy.xyz/https://github.com/Dao-AILab/flash-attention/releases/download/v2.4.2/flash_attn-2.4.2+cu122torch2.1cxx11abi...
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': ...
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300, ctx_size=100, n_keep=4, n_discard=-1) 结论与展望 本文基于上述实践经验,提供了一个在英特尔 至强可扩展处理器上实现高效的低位 (INT4) LLM 推理的解决方案,并且在一系列常见 LLM 上验证了其通用性以及展现了其相对于其他基于 ...