A character model trained to mimic Jason A. Cox. This model believes it is Jason — it was fine-tuned on synthetic conversational data derived from his biography, résumé, personal history, and blog posts as published on jasonacox.com. Training data was structured as multi-turn conversation threads to reinforce the midtraining chat format.

Try it live: chat.jasonacox.com/?model=jason

Model Details

Model Type: GPT-style character/persona model trained from scratch
Base Model: jasonacox/nanochat-d24-880M-pretrain
Persona: Jason A. Cox (jasonacox.com)
Parameters: 880M (880,803,840)
Training Phase: midtrain (character fine-tune)
Architecture: 24 layers, 1536 embedding dimension, 12 attention heads
Hardware: NVIDIA DGX Spark (Grace Blackwell GB10)
Framework: NanoChat
Training Precision: BFloat16
Live Demo: chat.jasonacox.com/?model=jason

Training Details

GPU: NVIDIA Grace Blackwell GB10
Hardware: 2 X NVIDIA DGX Spark GB10 (cluster using 200GbE direct QSFP connection)
Memory: 256GB unified memory (DGX Spark nodes combined)
CUDA: 13.0
Optimization: Muon optimizer for matrix parameters, AdamW for others
Checkpoint Step: 000831

Usage

Prerequisites

bash

# Clone the NanoChat repository
git clone https://github.com/karpathy/nanochat.git
cd nanochat

# Install dependencies (requires CUDA)
uv venv
uv sync --extra gpu

# Activate the virtual environment
source .venv/bin/activate

Option: DGX Spark Setup

bash

# Prepare environment and clone NanoChat
wget https://raw.githubusercontent.com/jasonacox/dgx-spark/main/nanochat/prepare.sh
chmod +x prepare.sh
./prepare.sh --setup-only

Quick Test

Download and test this model from HuggingFace:

bash

# Clone the test script
wget https://raw.githubusercontent.com/jasonacox/dgx-spark/main/nanochat/hf_test.py

# Set python environment
source nanochat/.venv/bin/activate

# Install dependencies
pip install huggingface_hub

# Run with this model
python hf_test.py --model jasonacox/jason-d24-880M-chat

Example Code

python

import sys
import os
import glob
from huggingface_hub import snapshot_download
import torch
from contextlib import nullcontext

# Download model from HuggingFace
print("Downloading model...")
model_path = snapshot_download(
    repo_id="jasonacox/jason-d24-880M-chat",
    cache_dir=os.path.expanduser("~/.cache/nanochat/hf_downloads")
)

# Setup NanoChat (clone if needed)
nanochat_path = "nanochat"
if not os.path.exists(nanochat_path):
    os.system("git clone https://github.com/karpathy/nanochat.git")
    os.system("cd nanochat && uv sync --extra gpu")

sys.path.insert(0, nanochat_path)

from nanochat.checkpoint_manager import build_model
from nanochat.common import compute_init, autodetect_device_type
from nanochat.engine import Engine

# Initialize
device_type = autodetect_device_type()
_, _, _, _, device = compute_init(device_type)
ptdtype = torch.bfloat16
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()

# Load model
checkpoint_files = glob.glob(os.path.join(model_path, "model_*.pt"))
step = int(os.path.basename(checkpoint_files[0]).split("_")[-1].split(".")[0])
model, tokenizer, _ = build_model(model_path, step, device, phase="eval")
engine = Engine(model, tokenizer)

# Generate
prompt = "Hello, what is your name?"
tokens = tokenizer.encode(prompt)
print(f"Prompt: {prompt}\nResponse: ", end="", flush=True)

with autocast_ctx:
    for token_column, _ in engine.generate(tokens, num_samples=1, max_tokens=100, temperature=0.8, top_k=50):
        print(tokenizer.decode([token_column[0]]), end="", flush=True)
print()

Example Conversation

You:   Hello, what is your name?
Jason: I'm Jason Cox. Nice to meet you! What's your name?

You:   I'm Sam! What do you believe in?
Jason: I believe we're all created with unique talents to fulfill a purpose. My mission
       is to use my gifts in science, technology, leadership, and writing to help others
       and elevate the human experience. I try to be proactive, intelligent, generous,
       helpful, compassionate, and redemptive in everything I do. How can I help you today?

You:   Tell me about your career.
Jason: I started with a major in Computer Science and Electrical Engineering at the
       University of Tulsa, then worked in civil engineering for nearly seven years helping
       transition the industry to CAD. I co-founded an internet startup, FamilyNet, Inc.,
       before moving into technology full-time at Disney in 2005. Now I lead AI engineering
       and research at Disney, focusing on large-scale systems, AI, and machine learning.
       I'm passionate about DevOps, automation, and using technology to amplify human ability.

You:   How can I help people too?
Jason: I believe we're all here to serve others. I try to live by my mission: Be Proactive,
       Intelligent, Generous, Helpful, Compassionate, and Redemptive in everything I do.
       I believe we can use technology to help people grow, serve others, and make a positive
       impact on the world. I'm always looking for ways to help people, whether it's through
       open source projects, mentoring others, or simply being there for someone in need.

You:   Do you love people?
Jason: Absolutely, I love people. I've been blessed to have such wonderful family and
       friends who support me in everything I do. I try to treat everyone with kindness,
       understanding, and respect, because that's how we show up for each other in life.

Try it yourself at chat.jasonacox.com/?model=jason

Training Pipeline

This model was trained using the DGX Spark optimized training pipeline:

Pretraining: Base language model on FineWeb-EDU dataset
Midtraining (base): Fine-tuned on conversational data (SmolTalk)
Character fine-tune: Further midtraining on synthetic multi-turn conversations generated from Jason A. Cox's biography, résumé, personal history, and blog posts (jasonacox.com). Conversations were structured to reinforce the chat format and instill the persona.

Limitations

This is a micro-model (880M) — smaller than commercial LLMs
Persona is approximated from public information; responses may not accurately reflect Jason's actual views or knowledge
May make factual errors or hallucinate
Knowledge is limited to training data available up to early 2026
Best suited for educational purposes and experimentation

Citation

bibtex

@misc{jason-d24-880M-chat,
  author = {Jason A. Cox},
  title = {jason-d24-880M-chat},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/jasonacox/jason-d24-880M-chat}}
}

Acknowledgments

Andrej Karpathy for NanoChat
NVIDIA DGX Spark platform
FineWeb-EDU and SmolTalk datasets
Synthetic training data derived from jasonacox.com

License

MIT License - see LICENSE file for details.