Megatron

MAX is Modular’s open and extensible AI inference framework for building and serving models. It supports thousands of model architectures across image, video, speech, and text modalities, exposes OpenAI-compatible endpoints, and can program deployments across multiple GPUs, nodes, and mixed hardware. MAX is part of Modular’s unified stack alongside Mojo, the company’s Pythonic systems language for CPUs, GPUs, and ASICs; Modular describes MAX as providing portable model serving and performance across different hardware platforms.

Transformers is Hugging Face's open-source model-definition framework and Python library for inference and training with pretrained text, computer vision, audio, video, and multimodal machine-learning models. It centralizes model definitions behind a unified API and provides the Pipeline interface, which preprocesses inputs and returns task-specific outputs for applications such as text generation, automatic speech recognition, image classification, and visual question answering. Models can be downloaded and cached from the Hugging Face Hub, and the library supports moving models between PyTorch, JAX, and TensorFlow workflows while exposing model internals for customization. It requires Python 3.10 or later and PyTorch 2.5 or later for the documented installation.

Miles is an enterprise-facing reinforcement learning framework for large-scale post-training of large language and vision-language models. It pairs SGLang for high-throughput, agentic rollouts with Megatron-LM for scalable training, and also provides a PyTorch FSDP2 backend for Hugging Face implementations. Its asynchronous architecture decouples rollout and training workers, supports configurable on- and off-policy schedules, and updates rollout engines in-loop through peer-to-peer RDMA weight transfer. The framework includes token-in-token-out data flow, Rollout Routing Replay for replaying mixture-of-experts routing decisions during training, fault-tolerant recovery of failed SGLang engines, low-precision training with MXFP8 and NVFP4 alongside FP8, INT4 QAT, BF16, and FP16, and LoRA or multi-LoRA training. It supports reinforcement-learning recipes including GRPO, GSPO, PPO, and REINFORCE++, as well as supervised fine-tuning, on-policy distillation, agentic environments, and diffusion-model training. Miles was forked from slime and integrates SGLang, Megatron-LM, and torch_memory_saver; the repository is released as an open-source project, though the provided page text does not state its license.

KTransformers is an open-source research framework for efficient large-language-model inference and fine-tuning through heterogeneous CPU-GPU computing. Its user-facing capabilities are provided through the kt-kernel source tree: high-performance inference and supervised fine-tuning (SFT). For mixture-of-experts models, it uses heterogeneous expert scheduling, placing frequently used or “hot” experts on GPUs and other experts on CPUs with optimized AMX, AVX512, or AVX2 kernels and NUMA-aware memory management. It supports CPU-side INT4/INT8 quantized weights, GPU-side GPTQ, native BF16 and FP8 precision, and multiple CPU, GPU, and NPU backends. The SFT capability integrates with LlamaFactory for hybrid CPU-GPU fine-tuning of large MoE models, including LoRA and full-parameter training, with INT4/INT8 quantization options.

MiniMax is an artificial-intelligence company and model provider offering large language models through hosted APIs, including models designed for coding, reasoning, and tool-using agent workflows. It is used as a model provider by agent frameworks such as Hermes Agent.