Instructions to use YOON1v/Apex-1-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YOON1v/Apex-1-DPO with Transformers:

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

pipe = pipeline("text-generation", model="YOON1v/Apex-1-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YOON1v/Apex-1-DPO")
model = AutoModelForCausalLM.from_pretrained("YOON1v/Apex-1-DPO", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YOON1v/Apex-1-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YOON1v/Apex-1-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YOON1v/Apex-1-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YOON1v/Apex-1-DPO

SGLang

How to use YOON1v/Apex-1-DPO 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 "YOON1v/Apex-1-DPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YOON1v/Apex-1-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "YOON1v/Apex-1-DPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YOON1v/Apex-1-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YOON1v/Apex-1-DPO with Docker Model Runner:
```
docker model run hf.co/YOON1v/Apex-1-DPO
```

APEX-1 (DPO)

A from-scratch 1.12B decoder-only LLM, pretrained on ~20B tokens and aligned with DPO.

Full code, architecture writeup, and training diary: github.com/DW-dev-UE/LLM-from-scratch — see BENCHMARK v2 for the full benchmark writeup this card summarizes.

This checkpoint is the DPO-aligned release (pretrain → SFT → DPO).

Model details


Architecture	Decoder-only Transformer, GQA (16Q/4KV) + RoPE (θ=500K) + SwiGLU + RMSNorm + QK-Norm, weight tying
Parameters	1,119.5M (measured)
Layers · d_model	24 · 2048
Context length	4096 (trained at 2048)
Vocab	32,000 (byte-level BPE, English-only)
Weights	stored as float32 (~4.48GB); load with `dtype=torch.bfloat16` to halve memory
Pretrain	51K steps · ~20B tokens · v3-en corpus (English web + code + math)
Post-training	SFT (8.4K steps) → RLVR attempted and abandoned (no learning signal at this scale — see `BENCHMARK-v2.md` §6) → DPO (60K preference pairs)
HF conversion	Weights map 1:1 onto `Qwen3ForCausalLM` (GQA+RoPE+SwiGLU+RMSNorm+QK-norm matches the Qwen3 block exactly). Verified by teacher-forced next-token argmax agreement against this model's own recorded benchmark generations, plus an A/B check confirming QK-norm is applied before RoPE (the Qwen3 convention)

Usage

This checkpoint expects the chat template below (system prompt + optional <THINKING> block) — see chat_template.jinja / ARCHITECTURE.md §7.1 in the repo for the exact format.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("YOON1v/Apex-1-DPO", dtype=torch.bfloat16).cuda()
tokenizer = AutoTokenizer.from_pretrained("YOON1v/Apex-1-DPO")

messages = [{"role": "user", "content": "Write a Python function that checks if a number is prime."}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Set enable_thinking=True to have the model reason inside <THINKING>...</THINKING> before answering. Per this model's own benchmark (see below), no-thinking mode currently scores higher — thinking mode tends to run long and cut into the answer budget.

Benchmarks

Standard suite (lm-evaluation-harness 0.4.12, bf16), compared against models trained on a similar token budget:

	Apex-1 SFT	Apex-1 DPO	TinyLlama-1.1B (3T tok)	Pythia-1.0B (300B tok)
Commonsense avg, 7 tasks (0-shot)	49.54	49.65	52.99	48.30
MMLU (5-shot)	24.83	24.90	25.34	25.70
GSM8K (5-shot, strict)	1.44	1.90	—	—
HumanEval (pass@1)	8.54	8.54	9.15	1.83
MBPP (pass@1)	4.80	5.20	—	—

DPO ≥ SFT on every metric above — no alignment tax. BoolQ 62.20 ranks 1st among TinyLlama-1.1B / Pythia-1.0B / OPT-1.3B. Apex-1 matches the 15×-more-trained Pythia-1.0B on commonsense average and beats it on ARC — the most token-efficient entry among same-budget peers, and its HumanEval score is 4.7× Pythia's despite the token gap.

Against the wider 2024–2025 small-model landscape (Llama-3.2-1B/9T, Qwen2.5-1.5B/18T, SmolLM2-1.7B/11T, OLMo-1B/2T), Apex-1 trails — expected given 450–900× less training data, not an architecture gap. Full comparison table, benchmark provenance, and citations: see BENCHMARK-v2.md §5.4 in the repo.

GSM8K/MBPP train splits partially overlap this model's SFT data (evaluation used the held-out test split); HumanEval is fully uncontaminated. See the repo's ckpt/lm_eval_Apex-1_COMPARISON.md for the raw numbers this card summarizes.

Known limitations

English-only (no multilingual pretraining data)
GSM8K / multi-step arithmetic reasoning is very weak (~2%) — this is why the project moved to DPO instead of RLVR for alignment (RLVR needs a non-trivial number of correct samples per group to produce a gradient; this model has too few)
MMLU sits at random-guess level (~25%), consistent with other 1B-scale models
Not instruction-tuned for multi-turn conversation beyond what the SFT/DPO mix covers

Training data

Pretrain: v3-en corpus (fineweb_edu, dclm, finemath4, cosmopedia2, code [python/js/java/c/cpp/sql/shell], finepdfs, wikipedia_en) — 80.8GB, ~20.3B tokens
SFT: HuggingFaceTB/smoltalk2 English subset, 538K examples (think:no-think ≈ 1:2)
DPO: 60K preference pairs

License

Apache 2.0.

Downloads last month: 30

Safetensors

Model size

1B params

Tensor type

F32

Evaluation results

acc_norm (0-shot) on HellaSwag
self-reported

46.910
acc (0-shot) on BoolQ
self-reported

62.200
pass@1 on HumanEval
self-reported

8.540
acc (5-shot, strict) on GSM8K
self-reported

1.900