AI大模型千问 qwen 中文文档�→below prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer "Content-Type: application/json" - �→d '{ "model": "Qwen/Qwen1.5-7B-Chat", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about chat_response = client.chat.completions.create( model="Qwen/Qwen1.5-7B-Chat", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about0 码力 | 56 页 | 835.78 KB | 1 年前3
PyTorch Release NotesPython libraries such as NumPy, SciPy, and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility explained in Running A Container and specify the registry, repository, and tags. About this task On a system with GPU support for NGC containers, when you run a container, the following occurs: ‣ The Docker documentation. Note: Starting in Docker 19.03, complete the steps below. The method implemented in your system depends on the DGX OS version that you installed (for DGX systems), the NGC Cloud Image that was0 码力 | 365 页 | 2.94 MB | 1 年前3
keras tutorialby Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy and install it immediately on your system. Keras Installation Steps Keras installation is quite easy. Follow below steps to properly install Keras on your system. Step 1: Create virtual environment Matplotlib Scipy Seaborn Hopefully, you have installed all the above libraries on your system. If these libraries are not installed, then use the below command to install one by one. numpy0 码力 | 98 页 | 1.57 MB | 1 年前3
《Efficient Deep Learning Book》[EDL] Chapter 7 - Automationreliance on statistical distributions to estimate the objective function which introduces noise in the system. Figure 7-3 (a) shows BOS for a two dimensional search space. It indicates that the search adaptively the combination elements represent the primitive and the combination operation choices. The count property of each STATE_SPACE element indicates the number of timesteps per block that state appears. For0 码力 | 33 页 | 2.48 MB | 1 年前3
动手学深度学习 v2.0Jean Kaddour, austinmw, trebeljahr, tbaums, Cuong V. Nguyen, pavelkomarov, vzlamal, NotAnother‐ System, J‐Arun‐Mani, jancio, eldarkurtic, the‐great‐shazbot, doctorcolossus, gducharme, cclauss, Daniel‐ 查询条件的结果进行排序。如今,搜索引擎使用机器学习和用户行为模型来获取网页相关性得分,很多学术 会议也致力于这一主题。 推荐系统 另一类与搜索和排名相关的问题是推荐系统(recommender system),它的目标是向特定用户进行“个性化” 推荐。例如,对于电影推荐,科幻迷和喜剧爱好者的推荐结果页面可能会有很大不同。类似的应用也会出现 在零售产品、音乐和新闻推荐等等。 在某些应用中,客户 idx_to_token[indices] return [self.idx_to_token[index] for index in indices] @property def unk(self): # 未知词元的索引为0 return 0 @property def token_freqs(self): return self._token_freqs def count_corpus(tokens):0 码力 | 797 页 | 29.45 MB | 1 年前3
TensorFlow on Yarn:深度学习遇上大数据K40 -->� <property> � yarn.nodemanager.resource.gpu-cores ((2,2)) � property>� � NodeManager上可用的GPU卡数是: 2 + 2 = 4� � � <property> � yarn yarn.nodemanager.resource.gpu-cores ((2,2) ,(2,2)) � property>� � NodeManager上可用的GPU卡数是: 2 + 2 + 2 + 2 = 8� � � TensorFlow on Yarn技术细节揭秘 NodeManager端GPU亲和性调度:�0 码力 | 32 页 | 4.06 MB | 1 年前3
Lecture 6: Support Vector Machineconcave, can be −∞ for some α, β Feng Li (SDU) SVM December 28, 2021 19 / 82 The Lower Bounds Property If α ⪰ 0, then G(α, β ) ≤ p∗, where p∗ is the optimal value of the primal problem Proof: If ˜ω0 码力 | 82 页 | 773.97 KB | 1 年前3
Lecture Notes on Support Vector Machinefunction regardless of the original problem; iii) G can be −∞ for some α and β Theorem 1. Lower Bounds Property: If α ⪰ 0, then G(α, β ) ≤ p∗ where p∗ is the optimal value of the (original) primal problem defined0 码力 | 18 页 | 509.37 KB | 1 年前3
微博在线机器学习和深度学习实践-黄波在线机器学习-实时模型训练 serving serving server server server worker Model Serving System Serving PS Traing PS Traing Model System Predict Score Sample Data worker worker worker 3 在线机器学习-参数服务器 serving PSsubmit File System checkpoint Model Training System Model register Status set/get Model delete Model Save Model Load HA Fault tolerance checkpoint Local HDFS Param Server System Model Serving Serving System 3 在线机器学习-参数服务器 • 参数规模 • 支持百亿特征维度,千亿参数 • 模型版本 • 多模型多版本:多组实验并行执行,提高实验迭代效率 • 在线版本切换:基于ZK的版本感知机制,动态进行版本切换,实现BASE模型的热更新,实时训练与离线训练周期模型融合 • 模型结构训练与推理兼容:在线PS与离线PS模型结构兼容,自动模型参数转换 • 稳定性优化 •0 码力 | 36 页 | 16.69 MB | 1 年前3
PyTorch Brand Guidelineswhen it is supported by the Symbol — a simple graphic that adds intrigue and curiosity to our system. The symbol allows us to speak through a more graphic language — without resorting to cliché cliché fire or data metaphors. 2 Brand Guidelines PyTorch Symbol Clearspace While our system encourages a flexible use of elements, it’s important to present the symbol in its entirety maintaining0 码力 | 12 页 | 34.16 MB | 1 年前3
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