How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
YaLM-100B is a large language model and open-weights AI model designed for generating and processing natural language text. It functions as a multilingual text generator optimized for producing and understanding human language content specifically in English and Russian.
The main features of yandex/yalm-100b are: Prompt-Based Text Generation, Decoder Architectures, Multilingual Text Generation, Large Language Models, Multilingual Text Embeddings, Natural Language Processing, Distribution-Based Sampling, Model Scaling.
Projects with overlapping indexed features include: thudm/chatglm2-6b — ChatGLM2-6B is an open-weight large language model designed for natural language conversations and text generation in… thudm/glm-130b — GLM-130B is a pre-trained foundation model and bilingual large language model designed for natural language processing… xai-org/grok-1 — Grok-1 is an open-weights large language model implementation featuring a sparse mixture-of-experts architecture. It… facebookresearch/llama — Llama is a large language model runtime and inference engine designed to load and execute autoregressive transformer… datawhalechina/so-large-lm — This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of… thudm/glm-4 — GLM-4 is an open weights large language model designed as a multimodal chat system. It functions as a…
ChatGLM2-6B is an open-weight large language model designed for natural language conversations and text generation in both English and Chinese. It functions as a bilingual chat model capable of processing and maintaining coherence across text sequences up to 32K tokens. The model is optimized for local deployment through precision quantization, which reduces memory requirements to allow execution on consumer-grade hardware. It supports distributing model weights across multiple graphics cards to handle parameters that exceed the memory of a single device. The project covers capabilities for
Grok-1 is an open-weights large language model implementation featuring a sparse mixture-of-experts architecture. It is designed for high-performance text generation and natural language processing by activating only a subset of specialized expert layers per token. The model utilizes 8-bit weight quantization to reduce memory overhead and accelerate loading. To manage its high parameter count, the implementation supports activation sharding, which distributes the memory load across multiple hardware devices during execution. The project covers large-scale model inference, including text comp
GLM-130B is a pre-trained foundation model and bilingual large language model designed for natural language processing tasks in both English and Chinese. It functions as an autoregressive language model and text generator capable of producing long-form content and predicting missing phrases. The model utilizes an autoregressive blank-filling architecture and a bidirectional dense transformer to process text. This approach allows the system to transition between understanding context through masked language modeling and generating sequential text using specific mask tokens. The project covers
Llama is a large language model runtime and inference engine designed to load and execute autoregressive transformer models. It enables the generation of natural language text completions from prompts using pretrained weights. The system features multi-GPU model parallelism, which distributes model weights and workloads across multiple graphics processors to support larger parameter counts. It also incorporates a content safety filter that uses classifiers to intercept and block unsafe inputs or outputs during the inference process. The project covers broad capabilities in distributed model