Model CoverageLarge Language Models

GPT-NeoX / Pythia

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GPT-NeoX is EleutherAI’s large-scale language model architecture. The same GPTNeoXForCausalLM architecture is used by the Pythia scaling suite, OpenAssistant, Databricks Dolly, and StableLM models.

TaskText Generation
ArchitectureGPTNeoXForCausalLM
Parameters1B – 20B
HF OrgEleutherAI

Available Models

  • GPT-NeoX-20B (EleutherAI)
  • Pythia suite: 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, 12B (EleutherAI)
  • OA-SFT-Pythia-12B (OpenAssistant)
  • Dolly-v2-12B (Databricks)
  • StableLM-tuned-alpha-7B (Stability AI)

Architecture

  • GPTNeoXForCausalLM

Example HF Models

Try with NeMo AutoModel

Install NeMo AutoModel and follow the fine-tuning guide to configure a recipe for this model.

1. Install (full instructions):

$pip install nemo-automodel

2. Clone the repo to get example recipes you can adapt:

$git clone https://github.com/NVIDIA-NeMo/Automodel.git
$cd Automodel

3. Fine-tune by adapting a base LLM recipe — override the model ID on the CLI:

$automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
>  --model.pretrained_model_name_or_path <MODEL_HF_ID>

Replace <MODEL_HF_ID> with the model ID from Example HF Models above.

1. Pull the container and mount a checkpoint directory:

$docker run --gpus all -it --rm \
>  --shm-size=8g \
>  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
>  nvcr.io/nvidia/nemo-automodel:26.06.00

2. The recipes are at /opt/Automodel/examples/ — navigate there:

$cd /opt/Automodel

3. Fine-tune:

$automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
>  --model.pretrained_model_name_or_path <MODEL_HF_ID>

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning

See the LLM Fine-Tuning Guide.

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