b4rtaz/distributed-llama की मुख्य विशेषताएं क्या हैं?

b4rtaz/distributed-llama की मुख्य विशेषताएं हैं: Distributed Inference Frameworks, Distributed Inference Engines, Distributed Model Execution, Tensor-Parallel Inference Distributions, Multi-Node, Large Language Model Deployments, Interactive Model Inference Sessions, Distributed Layer Synchronizers।

b4rtaz/distributed-llama के कुछ ओपन-सोर्स विकल्प क्या हैं?

b4rtaz/distributed-llama के ओपन-सोर्स विकल्पों में शामिल हैं: intel/ipex-llm — Intel XPU LLM Acceleration Library is a toolkit designed to accelerate large language model inference and finetuning… huggingface/text-generation-inference — Text Generation Inference is a production-ready engine designed for the deployment and serving of large language… paddlepaddle/fastdeploy — FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models.… zml/zml — zml is a machine learning model compiler and cross-platform inference engine that transforms model descriptions into… intel-analytics/bigdl — BigDL is a PyTorch acceleration framework and distributed inference engine designed for large language models. It… ztxz16/fastllm — fastllm is a set of specialized software components for model weight conversion, Mixture-of-Experts runtimes, and…

b4rtazdistributed-llama

Name: b4rtaz/distributed-llama
Author: b4rtaz

View on GitHub

2,837 स्टार्स210 फोर्क्सC++mit16 व्यूज़

Distributed Llama

Distributed-llama is a distributed inference engine and command line tool for running large language models across multiple networked machines. It functions as a compute cluster manager that coordinates worker nodes to share the computational load of a single model.

The system utilizes tensor parallelism to shard model weights across different hosts, allowing the execution of models that exceed the memory capacity of a single piece of hardware. It includes a dedicated format converter to transform standard model files into a compatible binary layout optimized for distributed loading.

The engine provides capabilities for multi-node model execution, worker node management, and text generation through a server interface or interactive chat sessions.

Features

Distributed Inference Frameworks - A distributed inference framework that runs large language models across multiple networked machines using tensor parallelism.
Distributed Inference Engines - An engine for splitting and executing large language model inference workloads across multiple networked nodes.
Distributed Model Execution - Executes large language model workloads spread across multiple networked machines to increase processing speed.
Tensor-Parallel Inference Distributions - Splits model weights across multiple network nodes using tensor parallelism to handle models exceeding single-device memory.

b4rtazdistributed-llama

View on GitHub

2,837 स्टार्स210 फोर्क्सC++mit16 व्यूज़

Distributed Llama

The engine provides capabilities for multi-node model execution, worker node management, and text generation through a server interface or interactive chat sessions.

Features

Distributed Inference Frameworks - A distributed inference framework that runs large language models across multiple networked machines using tensor parallelism.
Distributed Inference Engines - An engine for splitting and executing large language model inference workloads across multiple networked nodes.
Distributed Model Execution - Executes large language model workloads spread across multiple networked machines to increase processing speed.
Tensor-Parallel Inference Distributions - Splits model weights across multiple network nodes using tensor parallelism to handle models exceeding single-device memory.

Distributed Llama के ओपन-सोर्स विकल्प

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

intel/ipex-llm
intel/ipex-llm
8,836GitHub पर देखें
Intel XPU LLM Acceleration Library is a toolkit designed to accelerate large language model inference and finetuning on Intel CPUs, GPUs, and NPUs. It provides a distributed inference engine for scaling models across multiple accelerators, a multimodal model runtime for vision and speech tasks, and a low-bit model quantization tool for converting weights into INT4, FP8, and GGUF formats. The project features a parameter-efficient finetuning framework that enables model adaptation using QLoRA and DPO on Intel hardware. It distinguishes itself by providing specialized optimizations for Intel XP
Python
GitHub पर देखें8,836
huggingface/text-generation-inference
huggingface/text-generation-inference
10,775GitHub पर देखें
Text Generation Inference is a production-ready engine designed for the deployment and serving of large language models. It functions as a containerized runtime environment that manages model execution, scales across distributed hardware, and provides high-performance inference capabilities for demanding production environments. The project distinguishes itself through advanced optimization techniques, including continuous batching to maximize hardware utilization and tensor parallelism to shard large models across multiple accelerator cards. It supports efficient inference through custom com
Pythonbloomdeep-learningfalcon
GitHub पर देखें10,775
paddlepaddle/fastdeploy
PaddlePaddle/FastDeploy
3,700GitHub पर देखें
FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models. It provides the infrastructure to launch model services that process combined image, video, and text inputs, exposing these capabilities through a standardized, OpenAI-compatible API for chat and text completions. The project distinguishes itself through advanced inference pipeline engineering and GPU optimization. It employs speculative decoding, tensor parallelism, and a disaggregated execution model that separates prefill and decode phases across different hardware resourc
Pythonernieernie-45ernie-45-vl
GitHub पर देखें3,700
intel-analytics/bigdl
intel-analytics/BigDL
8,845GitHub पर देखें
BigDL is a PyTorch acceleration framework and distributed inference engine designed for large language models. It provides a toolkit for running models on Intel hardware, integrating quantization tools and libraries for parameter-efficient fine-tuning. The project distinguishes itself through the use of pipeline parallelism to distribute model workloads across multiple hardware accelerators. It utilizes low-bit integer quantization and speculative decoding to reduce memory footprints and decrease text generation latency. The system covers broad capabilities in model optimization, including w
Python
GitHub पर देखें8,845

Distributed Llama के सभी 30 विकल्प देखें

अक्सर पूछे जाने वाले प्रश्न

b4rtaz/distributed-llama क्या करता है?

Distributed Llama के ओपन-सोर्स विकल्प

intel/ipex-llm
intel/ipex-llm
8,836GitHub पर देखें
Intel XPU LLM Acceleration Library is a toolkit designed to accelerate large language model inference and finetuning on Intel CPUs, GPUs, and NPUs. It provides a distributed inference engine for scaling models across multiple accelerators, a multimodal model runtime for vision and speech tasks, and a low-bit model quantization tool for converting weights into INT4, FP8, and GGUF formats. The project features a parameter-efficient finetuning framework that enables model adaptation using QLoRA and DPO on Intel hardware. It distinguishes itself by providing specialized optimizations for Intel XP
Python
GitHub पर देखें8,836
huggingface/text-generation-inference
huggingface/text-generation-inference
10,775GitHub पर देखें
Text Generation Inference is a production-ready engine designed for the deployment and serving of large language models. It functions as a containerized runtime environment that manages model execution, scales across distributed hardware, and provides high-performance inference capabilities for demanding production environments. The project distinguishes itself through advanced optimization techniques, including continuous batching to maximize hardware utilization and tensor parallelism to shard large models across multiple accelerator cards. It supports efficient inference through custom com
Pythonbloomdeep-learningfalcon
GitHub पर देखें10,775
paddlepaddle/fastdeploy
PaddlePaddle/FastDeploy
3,700GitHub पर देखें
FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models. It provides the infrastructure to launch model services that process combined image, video, and text inputs, exposing these capabilities through a standardized, OpenAI-compatible API for chat and text completions. The project distinguishes itself through advanced inference pipeline engineering and GPU optimization. It employs speculative decoding, tensor parallelism, and a disaggregated execution model that separates prefill and decode phases across different hardware resourc
Pythonernieernie-45ernie-45-vl
GitHub पर देखें3,700
intel-analytics/bigdl
intel-analytics/BigDL
8,845GitHub पर देखें
BigDL is a PyTorch acceleration framework and distributed inference engine designed for large language models. It provides a toolkit for running models on Intel hardware, integrating quantization tools and libraries for parameter-efficient fine-tuning. The project distinguishes itself through the use of pipeline parallelism to distribute model workloads across multiple hardware accelerators. It utilizes low-bit integer quantization and speculative decoding to reduce memory footprints and decrease text generation latency. The system covers broad capabilities in model optimization, including w
Python
GitHub पर देखें8,845

Distributed Llama के सभी 30 विकल्प देखें

Distributed Llama

Features

Distributed Llama

Features

Distributed Llama के ओपन-सोर्स विकल्प

intel/ipex-llm

huggingface/text-generation-inference

PaddlePaddle/FastDeploy

intel-analytics/BigDL

अक्सर पूछे जाने वाले प्रश्न

स्टार हिस्ट्री

Distributed Llama के ओपन-सोर्स विकल्प

intel/ipex-llm

huggingface/text-generation-inference

PaddlePaddle/FastDeploy

intel-analytics/BigDL

अक्सर पूछे जाने वाले प्रश्न