这可以涵盖当前基于LLM的Text-to-SQL任务的研究。我们认为，一个完整的Text-to-SQL任务框架应包括以下四...
model:Qwen1.5-7B-Chat docker docker run -it --rm --gpus='"device=0,3"' -v /root/wangbing/model/Qwen-7B-Chat/V1/:/data/mlops/modelDir -v /root/wangbing/sftmodel/qwen/V1:/data/mlops/adapterDir/ -p30901:5000 -p7901:7860 dggecr01.huawei.com:80/tbox/text-generation-webui:at-...
Pure-text Autoregression:生成自然语言文本,通常用于语言模型训练 应用场景:文本生成、机器翻译、对话系统等。 技术:使用自回归模型(如 Transformer、LSTM)生成文本,模型逐词生成下一个词的概率分布。 Supervised Fine-tuning:通过指令微调对 Qwen-VL 预训练模型进行了微调,增强了其指令跟随和对话能力,从而产生了交互式 ...
1.Describe the current behavior / 问题描述 (Mandatory / 必填) 大致代码如下: streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) network.generate( inputs_id=inputs_id, max_length=max_length, streamer=streamer, do_sample=True, top_k=top_k, temperature=temperature, ...
请问下使用了lite方案,LLM和embedding模型都选择的qwen-turbo,启动和LLM问答正常,但是使用知识库问答,在使用了text-embedding-v1模型一直匹配不到知识库内容,这是为啥呢 Originally posted by @TonyHmx in #3100 (comment)
请问,Qwen-Coder的config.json与generation.json中eos_token_id均为151643:"<|endoftext|>",但加载Qwen-Coder的tokenizer后,eos_token_id为151645:"<|endoftext|>" :"<|im_end|>"。直接使用Qwen-Coder推理时,也是输出"<|im_end|>"结尾。请问要以哪个作为结束符更好。
Unlock free access to GPT-4o, o1-mini, o3-mini, DeepSeek-V3, DeepSeek-R1, Gemini 2.0, Qwen, Llama, Mistral & more! Explore cutting-edge AI models like DeepSeek & Gemini. No signup needed!
In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images. Starting from the Qwen-LM as a foundation, we endow it with visual capacity by the meticulously designed (i) visual receptor, (ii...
MiniMax-01:首个支持400万token上下文的模型 MiniMax-01:首个支持400万token上下文的开源大模型,首创lighting attention机制实现,实现逻辑类似于 Streaming-llm,超越qwen2.5-72b成为国产开源模型第一#minimax01 #mimimax开源模型 #qwen72b #国产开源模型 #MiniMaxText01 ...
We introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both text and images. Comprising Qwen-VL and Qwen-VL-Chat, these models exhibit remarkable performance in tasks like image captioning, question answering, visual localization,...