GPT-NeoX
EleutherAI framework and 20B-class models for training large autoregressive LMs with 3D parallelism—Apache-2.0 training stack.
Why it is included
Foundational open training codebase predating many commercial stacks.
Best for
Research groups customizing distributed LLM training.
Strengths
- Training stack
- Research heritage
- Eleuther ecosystem
Limitations
- Operating costs; not an app layer
Good alternatives
Megatron-LM · DeepSpeed
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PyTorch
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OLMo
Allen AI fully open LLM **pipeline**: weights, training code, data mixes, and evaluation—research transparency flagship.
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Pythia (Hugging Face)
EleutherAI’s public scaling suite: matched GPT-NeoX–architecture models from 70M–12B with public datasets for interpretability research.
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Axolotl
YAML-configured fine-tuning for LLMs: LoRA, QLoRA, FSDP, and many architectures on top of Hugging Face trainers.
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Unsloth
Optimized fine-tuning library claiming 2× faster LoRA/QLoRA with less VRAM via custom kernels and Hugging Face compatibility.
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BLOOM
BigScience 176B multilingual causal LM—landmark collaborative open training effort on Jean Zay (weights under BigScience Responsible AI License).
