higgsfield-ai/higgsfield

Health v2 · maintenance only

Adoption not part of the score

4106 stars · 705 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Higgsfield is an open-source GPU orchestration and machine learning framework for fault-tolerant, distributed training of very large models (billions to trillions of parameters), such as LLMs. It manages node allocation, experiment queuing, and deployment via GitHub and GitHub Actions while exposing a standard PyTorch/DeepSpeed workflow.

Use cases

• train a 70B LLaMA model across multiple GPUs
• orchestrate GPU clusters for deep learning experiments
• run distributed training with ZeRO-3 sharding
• queue and manage large-scale training experiments
• automate ML training pipelines with GitHub Actions
• fine-tune LLMs on a multi-node cluster

When to choose

• you need to train or fine-tune very large language models across multiple nodes
• you want fault-tolerant GPU orchestration with experiment queuing
• you prefer a plain PyTorch workflow with DeepSpeed/FSDP sharding
• you want training runs triggered and tracked through GitHub

When to avoid

• you only train small models on a single GPU
• you need a fully managed commercial training platform with support
• you need Kubernetes-native scheduling rather than GitHub-based deployment
• you require a project with frequent recent releases

Facets

framework · maturity maintenance

llm-training gpu-computing machine-learning deep-learning workflow-automation scheduling deep-learning large-language-models machine-learning gpu-computing python cloud distributed-training deepspeed zero-3 pytorch mlops cluster-management llama github-actions devops docker gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="higgsfield-ai/higgsfield")