Text Generation
Transformers
Safetensors
Korean
English
cohere2_vision
image-text-to-text
darwin
vidraft
delphi
chemistry
korean
Mixture of Experts
mixture-of-experts
cohere2_moe
218b
gpqa-88
conversational
Eval Results (legacy)
Eval Results
Instructions to use FINAL-Bench/Darwin-218B-Delphi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-218B-Delphi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-218B-Delphi") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-218B-Delphi") model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/Darwin-218B-Delphi") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FINAL-Bench/Darwin-218B-Delphi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FINAL-Bench/Darwin-218B-Delphi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-218B-Delphi", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FINAL-Bench/Darwin-218B-Delphi
- SGLang
How to use FINAL-Bench/Darwin-218B-Delphi 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 "FINAL-Bench/Darwin-218B-Delphi" \ --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": "FINAL-Bench/Darwin-218B-Delphi", "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 "FINAL-Bench/Darwin-218B-Delphi" \ --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": "FINAL-Bench/Darwin-218B-Delphi", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FINAL-Bench/Darwin-218B-Delphi with Docker Model Runner:
docker model run hf.co/FINAL-Bench/Darwin-218B-Delphi
File size: 7,902 Bytes
7b18ba7 8976b73 7b18ba7 a54f121 7b18ba7 c47eb81 a54f121 7b18ba7 8976b73 7b18ba7 8976b73 7b18ba7 8976b73 7b18ba7 8976b73 fc603d7 8976b73 fc603d7 8976b73 fc603d7 8976b73 c47eb81 8976b73 c47eb81 8976b73 c47eb81 8976b73 f2b1050 8976b73 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | ---
license: apache-2.0
language:
- ko
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- darwin
- vidraft
- delphi
- chemistry
- korean
- moe
- mixture-of-experts
- cohere2_moe
- 218b
- gpqa-88
base_model:
- FINAL-Bench/Darwin-218B-kr
- CohereLabs/command-a-plus-05-2026-bf16
base_model_relation: merge
datasets:
- FINAL-Bench/darwin-chem-data-v1
model-index:
- name: Darwin-218B-Delphi
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: GPQA Diamond
type: Idavidrein/gpqa
config: gpqa_diamond
metrics:
- type: accuracy
value: 88.1
name: Accuracy
---
# Darwin-218B-Delphi
> **VIDRAFT FINAL-Bench** — chemistry-specialized 218B MoE, served via the **DELPHI** 5-Phase inference cascade.
A chemistry-domain derivative of the Darwin-218B family. Built on the Korean-aligned base, distilled from a strong teacher with anti-contamination guarantees, and engineered for graduate-level scientific reasoning.
---
## 🏆 GPQA Diamond — Public Results
```
GPQA Diamond (198 questions) — Darwin-218B-Delphi
─────────────────────────────────────────────────────────────
Method | Accuracy
─────────────────────────────────────────────────────────────
Darwin-218B-Delphi baseline (MAJ@8) | 86.87% (172/198)
Darwin-218B-Delphi (DELPHI cascade) | 90.91% (180/198)
─────────────────────────────────────────────────────────────
DELPHI improvement | +4.04pp (+8 questions)
```
### Reference baselines (vendor-reported)
| Model | GPQA Diamond | Mode |
|------|-------------|------|
| GPT-5 (OpenAI) | 88.0% | thinking |
| Claude Opus 4.5 (Anthropic) | 91.8% | extended thinking |
| DeepSeek-V3.2 | ~78-82% | standard |
| **Darwin-218B-Delphi (MAJ@8)** | **86.87%** | **standard** |
| **Darwin-218B-Delphi (DELPHI)** | **90.91%** | **VIDRAFT signature** |
→ **DELPHI cascade로 Claude Opus 4.5 extended thinking 동급권** 진입.
---
## 🌳 Family Tree (족보)
```
🧓 GRANDFATHER (조부) 🧓 GRANDMOTHER (조모)
─────────────────── ───────────────────
CohereLabs/ Anthropic Claude
command-a-plus-05-2026-bf16 Opus 4.5
(Apache-2.0) (chemistry knowledge donor)
218B MoE / ~25B active via SFT distillation
128 experts, BF16 (no logits, output-only)
│ │
│ │
└────────────────┬──────────────────────┘
│
▼
👨 FATHER (부친) 👩 MOTHER (모친)
─────────────────── ───────────────────
FINAL-Bench/ FINAL-Bench/
Darwin-218B-kr darwin-chem-data-v1
(Korean LoRA merged) (993 chemistry CoT samples,
Korean fluency layer 6 sub-domains,
anti-contamination guaranteed)
│ │
│ │
└────────────────┬──────────────────────┘
│
▼
👦 CHILD (자식 / THIS MODEL)
──────────────────────────────
FINAL-Bench/Darwin-218B-Delphi
──────────────────────────────
• Korean + Chemistry specialist
• 218B MoE, ~25B active
• Apache-2.0
• GPQA Diamond 90.91% (DELPHI cascade)
• Served via DELPHI 5-Phase inference
```
### Lineage notes
- **Paternal line (모델 골격)**: Cohere Command A+ → Korean LoRA → Chemistry LoRA merge → Delphi
- **Maternal line (지식 source)**: Claude Opus 4.5 → 993 distilled chemistry CoT samples → Delphi's chemistry reasoning
- **Apache-2.0 compatibility**: All ancestors (paternal line) are Apache-2.0 licensed; maternal line is data-only output (Anthropic ToS compliant for derivative model training)
**Distillation**:
- Teacher: large frontier model (proprietary API; no logits exposure → SFT-on-outputs pattern)
- 993 high-quality chemistry CoT examples across 6 sub-domains:
organic, spectroscopy, physical, inorganic, analytical, special
- **Anti-contamination**: GPQA Diamond 198 questions guaranteed not in training data
- LoRA: r=16, α=32, q/k/v/o, lr=1e-5, 1 epoch, max_length=3072
- Trained on Darwin-218B-kr (S4 6×B200 bf16)
- Merge: full dense checkpoint, no runtime adapter loading
---
## Architecture
| Item | Value |
|------|-------|
| Total parameters | 218B |
| Active parameters | ~25B (MoE) |
| Experts | 128 (Cohere2 MoE) |
| Precision | BF16 |
| Architecture | `Cohere2VisionForConditionalGeneration` (multimodal-capable, text-primary) |
| Tokenizer | Cohere2 (vocab 256K) |
| Languages | English, Korean |
| Context | 65,536 tokens |
| License | Apache-2.0 |
---
## Usage
### vLLM (recommended)
```bash
vllm serve FINAL-Bench/Darwin-218B-Delphi \
--tensor-parallel-size 8 \
--dtype bfloat16 \
--max-model-len 65536 \
--trust-remote-code \
--enforce-eager \
--limit-mm-per-prompt '{"image":0,"video":0}'
```
Requires vLLM ≥ 0.21.0 (`Cohere2VisionForConditionalGeneration` support).
### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"FINAL-Bench/Darwin-218B-Delphi",
dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
tok = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-218B-Delphi")
messages = [
{"role": "user", "content": "Explain the SN2 mechanism step by step, "
"then justify why CH3I reacts faster than CH3Cl."}
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048, temperature=0.3, top_p=0.9)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```
---
## License
**Apache License 2.0**
Built upon `CohereLabs/command-a-plus-05-2026-bf16` (Apache-2.0) and `Darwin-218B-kr` (Apache-2.0). All upstream components are permissively licensed.
---
## Citation
```bibtex
@misc{darwin-218b-delphi-2026,
title = {Darwin-218B-Delphi: Chemistry-Specialized 218B MoE with DELPHI Cascade Inference},
author = {{VIDRAFT FINAL-Bench Team}},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-218B-Delphi}}
}
```
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