Instructions to use JulianKrgd/julian-600m-10b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JulianKrgd/julian-600m-10b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="JulianKrgd/julian-600m-10b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("JulianKrgd/julian-600m-10b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JulianKrgd/julian-600m-10b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JulianKrgd/julian-600m-10b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JulianKrgd/julian-600m-10b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JulianKrgd/julian-600m-10b

SGLang

How to use JulianKrgd/julian-600m-10b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "JulianKrgd/julian-600m-10b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JulianKrgd/julian-600m-10b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "JulianKrgd/julian-600m-10b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JulianKrgd/julian-600m-10b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JulianKrgd/julian-600m-10b with Docker Model Runner:
```
docker model run hf.co/JulianKrgd/julian-600m-10b
```

julian-600m-10b / README.md

JulianKrgd

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metadata

license: apache-2.0
language:
  - en
  - fr
tags:
  - text-generation
  - causal-lm
  - transformer
  - jax
  - flax
pipeline_tag: text-generation
datasets:
  - wikipedia
  - oscar-corpus/OSCAR-2301
  - bigcode/the-stack-dedup
library_name: transformers

Julian 500M (10B Tokens)

Decoder-only transformer trained from scratch on 10 billion tokens of English and French text.

This model is part of the Julian model family, a personal research project exploring LLM training from scratch. The goal is educational: understanding every step of the process, from data preparation to distributed training on TPUs.

Architecture

Parameters: 508M
Hidden size: 1280
Layers: 18
Attention heads: 20 (head dim 64)
FFN: 5120 (SwiGLU, 3 projections)
Context: 2048 tokens
Vocab: 50,000 (SentencePiece)

The model uses RoPE positional embeddings, RMSNorm, and has no bias terms in linear layers.

Training

Trained on TPU v5litepod-32 for approximately 8 hours.

Data mix:

Wikipedia (EN/FR): 50%
OSCAR web corpus: 30%
The Stack (code): 15%
Gutenberg books: 5%

Language ratio is roughly 70% English, 30% French.

Hyperparameters:

Batch size: 131k tokens
Learning rate: 6e-4 with cosine decay
Warmup: 3000 steps
AdamW (β2=0.95), weight decay 0.1

Final validation perplexity: 12.1

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("juliankerignard/julian-500m-10b")
tokenizer = AutoTokenizer.from_pretrained("juliankerignard/julian-500m-10b")

inputs = tokenizer("La France est", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.8)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

This is a base model trained on a modest amount of data. It will:

Generate repetitive or incoherent text on longer sequences
Hallucinate facts
Not follow instructions (no fine-tuning)

Not recommended for production use. Intended for research and experimentation.

Julian Model Family

Model	Parameters	Training Tokens	Status
julian-500m-10b	508M	10B	Released
julian-500m-39b	508M	39B	Training

Future models (1B+) are planned as the project evolves.

Acknowledgments

This project would not have been possible without the Google TPU Research Cloud (TRC) program, which provides free access to Cloud TPU resources for open research.

A sincere thank you to the TRC team for supporting independent researchers and making this kind of work accessible. If you're working on ML research, I highly encourage applying to the program.

More info: TPU Research Cloud

License

Apache 2.0

Citation

@misc{julian2026,
  author = {Kerignard, Julian},
  title = {Julian 500M},
  year = {2026},
  url = {https://huggingface.co/juliankerignard/julian-500m-10b}
}