Parameter	Value
Architecture	LlamaForCausalLM (decoder-only)
Parameters	~600M
Hidden size	1280
Layers	18
Attention heads	20
FFN size	5120 (SwiGLU)
Vocab size	50,000 (SentencePiece)
Context length	2048
Precision	bfloat16
Norm	RMSNorm
Position encoding	RoPE

Benchmark Results (0-shot)

Julian Models

Benchmark	Base (39B tokens)	SFT 30K	SFT 100K
HellaSwag (acc_norm)	53.5%	41.7%	41.6%
PIQA (acc)	66.8%	66.8%	66.6%
LAMBADA (acc)	37.3%	37.7%	37.7%
LAMBADA (ppl↓)	—	15.38	15.33
ARC Easy (acc)	—	53.5%	53.8%
ARC Challenge (acc_norm)	—	27.1%	26.7%
WinoGrande (acc)	—	53.8%	52.8%
BoolQ (acc)	—	60.6%	60.8%

SFT 30K and 100K yield near-identical benchmark scores. Additional SFT steps beyond 30K don't improve knowledge benchmarks. WinoGrande starts to degrade at 100K (likely overfitting on ~2.2 epochs).

Comparison with Other Models

Model	Params	Tokens	HellaSwag	PIQA	LAMBADA	ARC-E	ARC-C	WinoGrande
GPT-2 Small	124M	100B+	31.5%	—	46.0%	—	—	50.4%
OPT-125M	125M	300B	29.2%	63.0%	37.9%	43.5%	18.9%	50.3%
OPT-350M	331M	300B	32.0%	64.4%	45.2%	44.0%	20.7%	52.3%
Pythia-410M	405M	300B	33.3%	66.8%	50.5%	50.4%	21.3%	53.0%
Julian 600M SFT-100K	600M	39B+6.5B	41.6%	66.6%	37.7%	53.8%	26.7%	52.8%
Julian 600M Base	600M	39B	53.5%	66.8%	37.3%	—	—	—
GPT-2 XL	1558M	100B+	50.9%	70.8%	63.2%	—	—	59.4%
Pythia-1B	1B	300B	37.6%	70.5%	56.6%	55.9%	24.3%	54.5%
OPT-1.3B	1.3B	300B	41.5%	71.7%	57.9%	57.0%	23.4%	59.5%

Julian 600M Base outperforms OPT-1.3B on HellaSwag (53.5% vs 41.5%) despite being 2x smaller and trained on 8x fewer tokens. The SFT version trades some HellaSwag performance for instruction-following ability, while maintaining competitive scores on PIQA, ARC, and WinoGrande.

Sources: GPT-2 — OpenAI; OPT — Meta; Pythia — EleutherAI

Training

Base Model

Pre-training: ~40B tokens (70% EN / 30% FR)
Data: Wikipedia, OSCAR, Gutenberg, The Stack
Infrastructure: TPU v4-32, JAX/Flax

SFT Fine-tuning

Steps: 100,000 (from pretrained checkpoint_300000)
Dataset: 2.47M instruction examples (tokenized)
Batch size: 32 global (2/device × 4 devices × 4 hosts)
Sequence length: 2048
Epochs: ~2.2
Tokens seen: ~6.55B
Final loss: 1.69
Infrastructure: TPU v4-32, JAX/Flax

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("JulianKrgd/julian-600m-40b-instruct-sft100k", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("JulianKrgd/julian-600m-40b-instruct-sft100k")

inputs = tokenizer("Explain quantum computing in simple terms:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.8)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Small model (600M params) — limited reasoning and factual accuracy
Instruction following is basic compared to larger models
May hallucinate or generate incorrect information
Bilingual (EN/FR) but stronger in English
2.2 epochs over training data — some overfitting possible

Framework

Trained from scratch using JAX/Flax on Google Cloud TPU v4-32. Converted to HuggingFace safetensors format for compatibility.

License

Apache 2.0