---
license: apache-2.0
language: [en, ko, zh, ja, multilingual]
library_name: transformers
pipeline_tag: text-generation
tags:
- darwin
- darwin-v9
- darwin-jgos
- vidraft
- final-bench
- qwen
- qwen3.5
- qwen3_5_moe
- moe
- mixture-of-experts
- sparse-moe
- 397b
- a17b
- hybrid-attention
- linear-attention
- long-context
- 262k-context
- fp8
- w8a8
- compressed-tensors
- quantized
- reasoning
- reasoning-model
- thinking
- chain-of-thought
- cot
- math
- science
- stem
- code
- agentic
- tool-calling
- function-calling
- ztc
- zero-token-confidence
- confidence-estimation
- uncertainty-quantification
- hallucination-detection
- calibration
- self-verification
- selective-prediction
- pre-action-gating
- agent-safety
- llm-router
- guardrails
- gpqa
- gpqa-diamond
- mmlu-pro
- benchmark
- eval-results
- greedy
- korean
- english
- bilingual
- multilingual-llm
- vllm
- sglang
- openai-compatible
- multi-gpu
- h100
model-index:
- name: Darwin-397B-ZTC
results:
- task: {type: text-generation, name: Graduate-Level Reasoning}
dataset: {type: Idavidrein/gpqa, name: GPQA Diamond, config: gpqa_diamond, split: train}
metrics:
- {type: accuracy, value: 93.43, name: "Accuracy (greedy, single-sample)", verified: false}
---
# Darwin-397B-ZTC
### 397B Mixture-of-Experts built on Qwen 3.5 · **FP8** · GPQA Diamond **93.43 %** · **ZTC on board**
`reasoning` · `MoE` · `FP8` · `262K long context` · `Korean + English` · `hallucination detection` · `tool calling`
**Half the footprint, GPQA Diamond 93.43 %.
And this model stops itself before it acts on an answer it is about to get wrong.**
---
## 🧬 The Darwin Family
**Darwin** is [VIDRAFT](https://vidraft.net)'s measurement-driven reasoning model family —
roughly **20 official models**, **400+ community derivatives**, and a standing place among the
top open models on GPQA.
---
## 🧬 Darwin — transplanting the experts that work
A large MoE model is made of hundreds of **experts**.
**Darwin V9** selects the experts that perform best across several high-performing models,
transplants them onto a base backbone, and fuses them with trust-weighted evolutionary merging.
**Nothing is trained from scratch — proven capability is grafted on.**
That is why the same method holds across every model size.
| Model | Scale | GPQA Diamond |
|:---|:---|:---:|
| Darwin-9B-NEG | 9B | 84.3 |
| Darwin-27B-Opus | 27B dense | 86.9 |
| Darwin-36B-Opus | 36B MoE | 88.4 |
| Darwin-28B-Opus | 28B | 88.89 |
| Darwin-28B-REASON | 28B + DELPHI | 89.39 |
| Darwin-398B-JGOS | 397B MoE (bf16) | 90.9 |
| **Darwin-397B-ZTC** | **397B MoE (FP8)** | **93.43** |
### Lineage
| Role | | |
|:---|:---|:---|
| **Base** | `Qwen/Qwen3.5-397B-A17B` | 397B MoE backbone, ~17B active — Apache-2.0 |
| **Darwin V9** | expert transplant + trust-weighted evolutionary merging | this is where the model becomes Darwin |
| **Precision** | compressed-tensors W8A8 FP8 | 418.7 GB |
| **ZTC** | zero-token confidence readout | ships in `ztc/` |
- **Darwin V9** — evolutionary FFN/expert transplant and trust-weighted merging onto large MoE backbones
- **FINAL Bench** — VIDRAFT's evaluation framework
- **Four-layer Pre-AGI roadmap** — Darwin → AETHER → PROMETHEUS → HEPHAESTUS
---
## 🏛️ ZTC — it knows **before it answers**
Until now there were two ways to find out whether a model is about to be wrong.
Both of them only work **after the answer already exists**.
| Existing approach | Limitation |
|:---|:---|
| **Ask the model in words** | Costs extra tokens, adds latency, and models are badly overconfident |
| **Attach an external judge model** | **Two models to operate** · re-reads the entire answer · **degrades on long outputs** · 🔴 **arrives too late — the answer is already produced** |
**ZTC is a third path. It reads the model's own internal state once, before generation begins.**
| | External judge model | **ZTC** |
|:---|:---|:---|
| When | **After** the answer | **Before it starts** |
| Extra model | Required (two to operate) | **None (one)** |
| Extra generated tokens | Re-processes prompt + answer | **0** |
| Added latency | A second inference pass | **0.52 ms** — 0.003 % of generation cost |
| Long answers, long trajectories | **Degrades as length grows** | **Length-independent** |
---
### 📊 Measured — on this model
**① It judges its own answers** (PubMedQA, 539 items, 146 incorrect)
| | AUROC |
|:---|:---:|
| Self-reported confidence (asked in words) | 0.7646 |
| **ZTC (internal-state readout)** | **0.8801** |
| **Gain** | **+0.1155** |
Permutation null control: **z = 13.31** — shuffle the labels and the signal disappears.
**② It judges other models' answers** (Korean KMMLU, 400 items — law, math, biology, history)
| Judge | AUROC |
|:---|:---:|
| **Darwin-397B-ZTC** | **0.8228** (z = 9.66) |
| Qwen3.5-27B | 0.8171 |
| Qwen3.5-9B | 0.7297 |
| Qwen3.5-4B | 0.7284 |
| Open-source 4B judge model | 0.6844 |
**Same 400 items, same conditions: +0.138 over the open-source judge model.**
---
### 📦 The probe ships with this model
| File | |
|:---|:---|
| `ztc/ztc_probe_darwin397b.npz` | **45 KB** — the confidence readout for this model |
| `ztc/usage.py` | minimal, runnable example |
```python
z = np.load("ztc/ztc_probe_darwin397b.npz")
s = ((h - z["mu"]) / z["sd"]) @ z["w"] # h = last-token hidden state, 4096-dim
p = 1 / (1 + np.exp(-(z["cal_A"] * (s - z["s_mean"]) / z["s_std"] + z["cal_B"])))
```
One matrix product. No second model, no extra tokens, no network call.
The probe is specific to this model's hidden space (4096-dim) and does not transfer to others.
---
### 🤖 Why this is decisive for agents — **after-the-fact report vs. pre-action stop**
In an agent loop the expensive thing is not tokens. It is **actions**.
Files get edited, APIs get called, payments go through, mail leaves the building.
```
External judge : [generate] → [tool runs] → [cost, time, side effects] → [judge] → "that was wrong"
ZTC : [read state, 0.52 ms] → stop here if risky → the action never happens
```
**In front of an irreversible action, an after-the-fact verdict is an incident report.**
### Patterns
| Pattern | Behaviour |
|:---|:---|
| **Tool-call gating** | Low confidence → do not call the tool, ask a human instead |
| **Model routing** | Send only the low-confidence queries to a larger model or external API |
| **Retry budgeting** | Spend multi-sample decoding only on the steps that wobble |
| **Long-trajectory monitoring** | Agent trajectories run to tens of thousands of tokens — **length-independent, so it can stay on at every step** |
| **Selective prediction** | Withhold a risky answer and return "I don't know" |
### Gate deployment, measured
| Metric | Before | After |
|:---|:---:|:---:|
| Gate accuracy | 71.3 % | **93.3 %** |
| Incorrect answers blocked | 40.7 % | **74.1 %** |
| Expensive-path calls | 42 % | **17 %** |
At effectively zero cost it can stay on for **every** request.
**Use cases** — hallucination detection · uncertainty quantification · confidence calibration ·
selective prediction · routing risky queries upstream · **pre-action gating for agents**
---
## 🏆 GPQA Diamond 93.43 %
| Model | GPQA Diamond |
|:---|:---:|
| **Darwin-397B-ZTC** | **93.43** |
| GPT5.2 | 92.4 |
| Gemini-3 Pro | 91.9 |
| Qwen3.5-397B-A17B | 88.4 |
| Claude 4.5 Opus | 87.0 |
```
GPQA Diamond, all 198 items · greedy · single sample · no test-time engine
```
*Comparison figures: Qwen3.5-397B-A17B official model card.*
---
## ⚙️ Specifications
| Item | Value |
|:---|:---|
| Architecture | `Qwen3_5MoeForConditionalGeneration` |
| Parameters | **397 B total / 17 B active** (512 experts, 10 routed + 1 shared per token) |
| Layers · hidden | 60 · 4096 |
| Attention | Hybrid (45 linear + 15 full attention layers) |
| **Precision** | **FP8** (compressed-tensors W8A8) |
| Size on disk | **418.7 GB** |
| Context | **262,144 tokens** |
| License | apache-2.0 |
---
## 🚀 Quickstart
### Serving with vLLM (4 × H100 80GB)
```bash
vllm serve FINAL-Bench/Darwin-397B-ZTC \
--served-model-name darwin-397b \
--tensor-parallel-size 1 --pipeline-parallel-size 4 \
--gpu-memory-utilization 0.92 --max-model-len 262144 \
--cpu-offload-gb 20 --enforce-eager --trust-remote-code \
--reasoning-parser qwen3 --enable-auto-tool-choice \
--port 8000
```
### SGLang
```bash
python -m sglang.launch_server --model-path FINAL-Bench/Darwin-397B-ZTC \
--port 8000 --tp-size 8 --context-length 262144
```
### Chat Completions (OpenAI-compatible)
```python
from openai import OpenAI
c = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
r = c.chat.completions.create(
model="darwin-397b",
messages=[{"role": "user", "content": "Why is the Riemann hypothesis hard?"}],
temperature=0.0, max_tokens=8192,
)
m = r.choices[0].message
print(m.reasoning_content) # thinking trace
print(m.content) # final answer
```
### 🛠️ Tool calling
```python
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city",
"parameters": {"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"]},
},
}]
r = c.chat.completions.create(
model="darwin-397b", tools=tools,
messages=[{"role": "user", "content": "What's the weather in Paris?"}],
)
print(r.choices[0].message.tool_calls)
```
### 🤖 Agents and coding CLIs
The endpoint is OpenAI-compatible, so **existing tooling connects unchanged.**
opencode — `~/.config/opencode/opencode.json`
```json
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"darwin": {
"npm": "@ai-sdk/openai-compatible",
"name": "Darwin (local)",
"options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" },
"models": { "darwin-397b": { "name": "Darwin-397B-ZTC" } }
}
}
}
```
Any OpenAI-compatible client (Cline, Continue, Aider, …)
```bash
export OPENAI_BASE_URL=http://localhost:8000/v1
export OPENAI_API_KEY=EMPTY
export OPENAI_MODEL=darwin-397b
```
---
## 🎯 Intended use
- Graduate-level STEM reasoning (GPQA, science qualifying exams)
- Mathematics and long multi-step chains of thought
- Code generation and debugging
- 🤖 **Agent workflows** — ZTC blocks irreversible tool calls **before** they run
- **Bilingual Korean + English reasoning** (Chinese and Japanese supported)
- **Work where a wrong answer is expensive** — ZTC filters risky answers before they ship
## 🔗 Links
- 🌐 **[vidraft.net](https://vidraft.net)** — VIDRAFT
- 🤗 **[FINAL-Bench](https://huggingface.co/FINAL-Bench)** — all models
- 📱 **[POCKET](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6)** — on-device line that runs on phones and GPU-less PCs
## 📚 Citation
```bibtex
@misc{darwin397b_ztc_2026,
title = {Darwin-397B-ZTC: FP8 Mixture-of-Experts with Zero-Token Confidence},
year = {2026},
url = {https://vidraft.net},
note = {Base: Qwen/Qwen3.5-397B-A17B}
}
```