Model CoverageLarge Language Models (LLMs)

GPT-J

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GPT-J is a 6B parameter transformer language model trained by EleutherAI on the Pile dataset. It was one of the earliest large open-weight models.

TaskText Generation
ArchitectureGPTJForCausalLM
Parameters6B
HF OrgEleutherAI

Available Models

  • gpt-j-6b: 6B parameters
  • gpt4all-j: GPT-J fine-tuned for instruction following (Nomic AI)

Architecture

  • GPTJForCausalLM

Example HF Models

ModelHF ID
GPT-J 6BEleutherAI/gpt-j-6b
GPT4All-Jnomic-ai/gpt4all-j

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:26.04.00

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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