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...
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300) 03性能测试 经过持续努力,上述优化方案的 INT4 性能得到了显著提升。本文在搭载英特尔 至强 铂金 8480+ 的系统上与 llama.cpp 进行了性能比较;系统配置详情如下:@3.8 GHz,56 核/路,启用超线程,启用睿频,总内存 256 GB (16 x 16 G...
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) >>> inputs = processor(text=prompt, images=image, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs, max_length=30) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "\...
get(url, stream=True).raw).convert("RGB") >>> prompt = "What is unusual about this image?" >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device) >>> outputs = model.generate( ... **inputs, ... do_sample=False, ... num_beams=5, ... max_...
调用generate并解码预测。 generated_ids = model.generate(pixel_values=pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_caption) a drawing of a pink and blue pokemon 看起来微调的模型生成了一个相当不错的字幕! Tran...
splitter will sample a window of# context length + lags + prediction length (from the 366 possible transformed time series)# randomly from within the target time series and return an iterator.stream = Cyclic(transformed_data).stream()training_ins...
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': ...