Model details
Table | |
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| Architecture | Decoder-only MoE Transformer, every layer MoE. GQA (16Q/4KV, head_dim 128) + QK-RMSNorm + RoPE (θ=10K) + SwiGLU experts + RMSNorm, weight tying |
| Parameters | 3,869.1M total · 1,453.2M active per token (1,142.0M non-embedding active), measured |
| Layers · d_model | 32 · 2048 |
| Experts | 16 per layer, top-4 routing with renormalized probabilities, expert width 1024 |
| Router losses | load-balancing aux loss 0.01 (global-batch statistics) + router z-loss 1e-3 |
| Context length | 4096 |
| Vocab | 151,936 (Qwen3 tokenizer), tied embeddings |
| Weights | stored as bfloat16 (~7.7GB), cast from the float32 training export |
| Pretrain | 54,250 steps · 86.5B tokens (web · code in 11 languages · math · curated), 1M-token batches; the last 20.4B tokens are an LR decay phase (web 30 / code 35 / math 15 / curated 20); parts trained with DiLoCo on 2× GH200 |
| Post-training | SFT stage 1: 2.21B tokens (general 29 / code 44 / math 27). SFT stage 2: 0.28B tokens × 2 epochs (execution-verified code, competition math, precise instruction following) |
| HF format | Weights map 1:1 onto Qwen3MoeForCausalLM (the training model uses fused expert tensors; the export splits them per expert). Export check: logits relative difference 1.0e-6 and 100% next-token agreement against the training checkpoint |
Usage
The model uses the ChatML template (<|im_start|>role\n...<|im_end|>) shipped in tokenizer_config.json; generation ends at <|im_end|>.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("YOON1v/Apex-2", dtype=torch.bfloat16, device_map="cuda")tokenizer = AutoTokenizer.from_pretrained("YOON1v/Apex-2") messages = [{"role": "user", "content": "Write a Python function that checks if a number is prime."}]inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")out = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.9)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
vLLM loads it directly as well: LLM(model="YOON1v/Apex-2", dtype="bfloat16", max_model_len=4096).
Benchmarks
Measured by us with vLLM greedy decoding and the chat template (prompt + answer ≤ 4096 tokens). Model-written code was executed in a sandbox.
Table with columns: Area, Benchmark, Apex-2 (SFT), Base| Area | Benchmark | Apex-2 (SFT) | Base |
|---|
| Code | HumanEval | 43.9 | 36.6 |
| Code | HumanEval+ | 41.5 | 32.9 |
| Code | MBPP | 56.3 | 54.8 |
| Code | MBPP+ | 48.9 | 46.3 |
Compared with similar-size models (official numbers; protocols differ, so this is a rough comparison):
Table with columns: Model, Pretraining tokens, HumanEval, HumanEval+, MBPP, MBPP+, GSM8K, IFEval, MMLU| Model | Pretraining tokens | HumanEval | HumanEval+ | MBPP | MBPP+ | GSM8K | IFEval | MMLU |
|---|
| Apex-2 (3.87B · 1.45B active) | 0.087T | 43.9 | 41.5 | 56.3 | 48.9 | 32.4 | 44.7 | 28.6 |
| Apex-1 DPO (1.1B dense) |
DeepSeek-Coder numbers are the Qwen2.5-Coder report's re-evaluation. * Different IFEval metric: Apex-2 and Qwen report prompt-level strict; the others report an average of metrics or do not specify.
With 1/23 to 1/400 of the pretraining data of these models, Apex-2's base model matches Qwen2.5-1.5B (18T tokens) on HumanEval+ (32.9 vs 32.9). The big gaps are knowledge (MMLU) and math, which mainly track pretraining scale.
[!NOTE]
SFT data was 13-gram decontaminated against HumanEval(+), MBPP(+), GSM8K test, MATH, MMLU, ARC, HellaSwag, PIQA, WinoGrande, IFEval and LAMBADA. For LiveCodeBench only the newer v5–v6 problems (after 2024-08) were used, to reduce contamination risk.
Why no DPO: DPO on allenai/Dolci-Instruct-DPO (220K pairs) made answers 2.3× longer (320 → 733 tokens on average). Chosen-answer likelihood also fell during training. In evaluation HumanEval+ went 41.5 → 32.3, MBPP+ 48.9 → 37.3, GSM8K 32.4 → 14.3 and IFEval 44.7 → 35.7, so this SFT checkpoint is the release.
Known limitations
- English-centric. Other languages, including Korean, barely work (multilingual data was excluded from SFT).
- Limited world knowledge (MMLU ~29%), so it often states wrong facts with confidence.
- Weak at competitive programming (LiveCodeBench medium ~1%) and at predicting code execution (CRUXEval).
- 4096-token context. No tool use, no thinking mode.
Training data
- Pretrain: 40 deduplicated sources, including:
- web: FineWeb-Edu, DCLM, FinePDFs
- code: StarCoder-family and The Stack-derived code in 11 languages, OpenCoder corpora, synthetic code
- math: FineMath, InfiWebMath, Nemotron-CC-Math
- curated: Wikipedia, StackExchange, peS2o, Cosmopedia, Gutenberg
- SFT:
- general: allenai/Dolci-Instruct-SFT (tool-use, multilingual and identity subsets removed)
- code: OpenCoder opc-sft-stage1/2, nvidia/OpenCodeInstruct (unit-test pass rate ≥ 0.9), bigcode self-oss-instruct, m-a-p/Code-Feedback
- math: nvidia/OpenMathInstruct-2, AI-MO/NuminaMath-CoT
Some SFT sources contain synthetic data generated by third-party models (e.g. GPT-4-class, Llama-3.1-405B, Qwen2.5-Coder); check each dataset's terms for your use case.
License
Apache 2.0.