deepseek-ai/deepseek-llm की मुख्य विशेषताएं क्या हैं?

deepseek-ai/deepseek-llm की मुख्य विशेषताएं हैं: Causal Language Modeling, Complex Problem Solving, Conversational AI, Large Language Models, Sparse Routing Architectures, Causal Masking, Grouped-Query Attention, Latent Attention Mechanisms।

deepseek-ai/deepseek-llm के कुछ ओपन-सोर्स विकल्प क्या हैं?

deepseek-ai/deepseek-llm के ओपन-सोर्स विकल्पों में शामिल हैं: nlpxucan/wizardlm — WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding,… stability-ai/stablelm — StableLM is a pre-trained transformer-based large language model designed for natural language generation and… thudm/chatglm3 — ChatGLM3 is an open-weights large language model designed for bilingual conversational interactions in English and… zai-org/glm-4 — GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning,… internlm/internlm — InternLM is a large language model and a comprehensive suite of weights designed for text generation and complex… databrickslabs/dolly — Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates…

Deepseek LLM

DeepSeek-LLM एक लार्ज लैंग्वेज मॉडल और कॉज़ल लैंग्वेज मॉडल है जिसे नेचुरल लैंग्वेज जनरेशन के लिए डिज़ाइन किया गया है। यह टेक्स्ट कंप्लीशन और संवादात्मक जनरेशन करने के लिए एक अनुक्रम में अगले टोकन की भविष्यवाणी करने में सक्षम एक बहुभाषी सिस्टम के रूप में कार्य करता है।

यह मॉडल तार्किक तर्क के लिए विशेष है, विशेष रूप से एक कोड और गणित LLM के रूप में। यह इसे जटिल समस्या समाधान करने में सक्षम बनाता है, जिसमें निष्पादन योग्य कोड उत्पन्न करना और चरण-दर-चरण विश्लेषण के माध्यम से गणितीय समीकरणों को हल करना शामिल है।

सिस्टम की व्यापक क्षमताएं संवादात्मक AI को कवर करती हैं, जिसमें कई भाषाओं में चैट कंप्लीशन और टेक्स्ट अनुक्रमों का निर्माण शामिल है। इसकी कार्यात्मक सतह स्वचालित कोड जनरेशन और विभिन्न लेखन कार्यों के लिए सुसंगत टेक्स्ट के उत्पादन तक फैली हुई है।

Features

Causal Language Modeling - Functions as a causal language model that predicts the next token for text completion and conversation.
Complex Problem Solving - Applies advanced reasoning and step-by-step analysis to solve complex mathematical equations.
Conversational AI - Provides a system for interactive, context-aware dialogue generation across multiple languages.
Large Language Models - Operates as a large language model trained on massive datasets for complex reasoning and generation.
Sparse Routing Architectures - Employs a mixture-of-experts architecture to scale parameter count without increasing computational cost.
Causal Masking - Implements unidirectional causal masking to enable autoregressive text generation during training.
Grouped-Query Attention - Utilizes grouped-query attention to reduce memory bandwidth requirements for large batch inference.
Latent Attention Mechanisms - Compresses key-value caches into latent vectors to optimize memory usage and throughput during inference.
Multilingual Language Models - Supports text processing and generation across multiple different languages and scripts.
Natural Language Code Generators - Translates natural language descriptions into executable source code to solve technical programming challenges.
Natural Language Generation - Produces coherent and contextually relevant natural language text for diverse writing tasks.
RMSNorm Layers - Uses root mean square layer normalization to stabilize training and accelerate convergence.
Rotary Positional Embeddings - Applies rotary positional embeddings to maintain long-context coherence through relative token positioning.
Reasoning And Math Models - Specializes in logical reasoning for mathematical problem solving and executable code production.
Chat Completion Services - Generates human-like dialogue through structured conversational turn sequences.
Text Sequence Generation - Predicts subsequent tokens in a text stream to perform natural language completion.
Foundation Models - Language model trained from scratch on massive datasets.
General Purpose Models - High-performance base and chat models for diverse language tasks.

स्टार हिस्ट्री

deepseek-aideepseek-LLM

Name: deepseek-ai/deepseek-llm
Author: deepseek-ai

View on GitHub

7,100 स्टार्स1,100 फोर्क्सMakefileMIT14 व्यूज़chat.deepseek.com

Deepseek LLM

Features

Causal Language Modeling - Functions as a causal language model that predicts the next token for text completion and conversation.
Complex Problem Solving - Applies advanced reasoning and step-by-step analysis to solve complex mathematical equations.
Conversational AI - Provides a system for interactive, context-aware dialogue generation across multiple languages.
Large Language Models - Operates as a large language model trained on massive datasets for complex reasoning and generation.
Sparse Routing Architectures - Employs a mixture-of-experts architecture to scale parameter count without increasing computational cost.
Causal Masking - Implements unidirectional causal masking to enable autoregressive text generation during training.
Grouped-Query Attention - Utilizes grouped-query attention to reduce memory bandwidth requirements for large batch inference.
Latent Attention Mechanisms - Compresses key-value caches into latent vectors to optimize memory usage and throughput during inference.
Multilingual Language Models - Supports text processing and generation across multiple different languages and scripts.
Natural Language Code Generators - Translates natural language descriptions into executable source code to solve technical programming challenges.
Natural Language Generation - Produces coherent and contextually relevant natural language text for diverse writing tasks.
RMSNorm Layers - Uses root mean square layer normalization to stabilize training and accelerate convergence.
Rotary Positional Embeddings - Applies rotary positional embeddings to maintain long-context coherence through relative token positioning.
Reasoning And Math Models - Specializes in logical reasoning for mathematical problem solving and executable code production.
Chat Completion Services - Generates human-like dialogue through structured conversational turn sequences.
Text Sequence Generation - Predicts subsequent tokens in a text stream to perform natural language completion.
Foundation Models - Language model trained from scratch on massive datasets.
General Purpose Models - High-performance base and chat models for diverse language tasks.

Deepseek LLM के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Deepseek LLM के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

nlpxucan/wizardlm
nlpxucan/WizardLM
9,486GitHub पर देखें
WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding, mathematical, and conversational tasks. It functions as an AI system for mathematical reasoning and code generation, as well as a synthetic dataset generator used to train other language models. The project is distinguished by its evolutionary instruction tuning, which uses a method to rewrite simple instructions into complex tasks. This process expands training dataset difficulty and produces a high volume of open-domain tasks across various difficulty levels. The system covers capa
Python
GitHub पर देखें9,486
stability-ai/stablelm
Stability-AI/StableLM
15,699GitHub पर देखें
StableLM is a pre-trained transformer-based large language model designed for natural language generation and zero-shot inference. It functions as a causal language model that predicts the next token in a sequence to produce human-like text for conversational and creative writing tasks. The model is built as a fine-tunable base, allowing the adaptation of pre-trained weights to specific tasks or styles through custom dataset training and weight regularization. It utilizes rotary positional embeddings and flash-attention to optimize memory usage and processing efficiency during deployment on G
Jupyter Notebook
GitHub पर देखें15,699
thudm/chatglm3
THUDM/ChatGLM3
13,676GitHub पर देखें
ChatGLM3 is an open-weights large language model designed for bilingual conversational interactions in English and Chinese. It functions as a tool-augmented system capable of calling external functions and executing internal code to resolve complex tasks. The model utilizes four-bit quantization to reduce memory requirements, enabling inference on consumer hardware and diverse processing units including GPUs and CPUs. It features an expanded context window for processing and summarizing long documents and includes a supervised fine-tuning pipeline for adapting the model to specialized domains
Python
GitHub पर देखें13,676
zai-org/glm-4
zai-org/GLM-4
7,058GitHub पर देखें
GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning, and multilingual conversation. It functions as a multimodal system capable of processing high-resolution visual content and as a long-context model designed to analyze documents with a context window of up to one million tokens. The project differentiates itself through a function calling interface that enables AI agent development by connecting the model to external APIs and real-time web browsing. It includes specialized capabilities for generating functional programming cod
Pythonchatglmchatglm-6bglm
GitHub पर देखें7,058

Deepseek LLM के सभी 30 विकल्प देखें

अक्सर पूछे जाने वाले प्रश्न

deepseek-ai/deepseek-llm क्या करता है?

Deepseek LLM

Features

स्टार हिस्ट्री

Deepseek LLM

Features

Deepseek LLM के ओपन-सोर्स विकल्प

nlpxucan/WizardLM

Stability-AI/StableLM

THUDM/ChatGLM3

zai-org/GLM-4

अक्सर पूछे जाने वाले प्रश्न

स्टार हिस्ट्री

Deepseek LLM के ओपन-सोर्स विकल्प

nlpxucan/WizardLM

Stability-AI/StableLM

THUDM/ChatGLM3

zai-org/GLM-4

अक्सर पूछे जाने वाले प्रश्न