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configs | ||
figs | ||
build_map.py | ||
clean.py | ||
create_hostname.sh | ||
data.py | ||
env.yaml | ||
eval_figures.py | ||
eval_self_instruct.py | ||
generate.py | ||
GPT-J_MAP.md | ||
inference.py | ||
launcher.sh | ||
old-README.md | ||
read.py | ||
README.md | ||
requirements.txt | ||
train.py | ||
TRAINING_LOG.md |
Training GPT4All-J
Please see GPT4All-J Technical Report for details.
GPT4All-J Training Data
- We are releasing the curated training data for anyone to replicate GPT4All-J here: GPT4All-J Training Data
We have released updated versions of our GPT4All-J
model and training data.
v1.0
: The original model trained on the v1.0 datasetv1.1-breezy
: Trained on a filtered dataset where we removed all instances of AI language modelv1.2-jazzy
: Trained on a filtered dataset where we also removed instances like I'm sorry, I can't answer... and AI language model
The models and data versions can be specified by passing a revision
argument.
For example, to load the v1.2-jazzy
model and dataset, run:
from datasets import load_dataset
from transformers import AutoModelForCausalLM
dataset = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision="v1.2-jazzy")
model = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-j", revision="v1.2-jazzy")
GPT4All-J Training Instructions
accelerate launch --dynamo_backend=inductor --num_processes=8 --num_machines=1 --machine_rank=0 --deepspeed_multinode_launcher standard --mixed_precision=bf16 --use_deepspeed --deepspeed_config_file=configs/deepspeed/ds_config_gptj.json train.py --config configs/train/finetune_gptj.yaml