Instructions to use N8Programs/imagegpt-large-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use N8Programs/imagegpt-large-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir imagegpt-large-bf16 N8Programs/imagegpt-large-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
metadata
library_name: mlx
tags:
- imagegpt
- vision
- mlx
- safetensors
- bfloat16
- icl-many-replication
datasets:
- imagenet-21k
base_model: openai/imagegpt-large
ImageGPT Large BF16 MLX
This repository contains the local bfloat16/MLX conversion of
openai/imagegpt-large used by
the ImageGPT runs in the ICLManyReplication artifact for "Many Next-Token
Predictors are In-Context Learners".
The upload includes:
model.safetensors: converted bfloat16 model weightsconfig.json: ImageGPT model configuration with the local MLX model file hookimagegpt_mlx_lm.py: local MLX model implementation used by the replication harnesspreprocessor_config.json: original ImageGPT preprocessing configurationconversion_metadata.json: source path, dtype, tensor count, and conversion notes
The conversion metadata records openai/imagegpt-large weights converted from
the original PyTorch checkpoint into a bfloat16 safetensors artifact.
Intended Use
This checkpoint is intended as a drop-in artifact for reproducing the ImageGPT
principal-result row in ICLManyReplication. For general ImageGPT usage, see the
upstream openai/imagegpt-large
model card.