Instructions to use FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8 with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8")
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8"
# 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-9B-NEG-x-Negentropy-V8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8

SGLang

How to use FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8 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-9B-NEG-x-Negentropy-V8" \
    --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-9B-NEG-x-Negentropy-V8",
		"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-9B-NEG-x-Negentropy-V8" \
        --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-9B-NEG-x-Negentropy-V8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8 with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Darwin-9B-NEG-x-Negentropy-V8

World-first hybrid: NEG (Native Entropy Gating) × Trace Inversion

This is a Darwin V8 (Gen4) evolutionary FFN merge between two leading 9B reasoning models:

Role	Model	Contribution
Mother	FINAL-Bench/Darwin-9B-NEG	Native Entropy Gating (4M-param confidence head, GPQA 84.34% with 3-stage ensemble)
Father	Jackrong/Negentropy-claude-opus-4.7-9B	Claude Opus 4.7 reasoning chains via Trace Inversion (arXiv:2603.07267)

Method (Darwin V8 Gen4 engine)

In-memory FFN swap + 21-point grid search on (CLIcK + KMMLU + GPQA):

Mother weights: (1 − α)
Father FFN (gate_proj / up_proj / down_proj only): α
Optimal α = 0.85 selected by total weighted score
All non-FFN tensors (attention, embeddings, NEG-Head, NEG-Gate, vision tower, MTP head) inherited from Mother → NEG modules preserved intact

Benchmarks (logit-based, NEG OFF baseline)

Metric	Score
CLIcK (Korean QA, n=200)	68.5%
KMMLU (Korean MMLU, n=200)	52.5%
GPQA Diamond (n=50)	48.0%
TOTAL (weighted)	55.50

Note: Above scores are with NEG gating disabled (single-forward logit comparison). With NEG generation gating active (the Mother's signature mechanism), GPQA Diamond is expected to reach 60%+ at 1× inference cost (NEG-only Mode 1: 63.64% on the unmerged Mother).

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("VIDraft/Darwin-9B-NEG-x-Negentropy-V8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "VIDraft/Darwin-9B-NEG-x-Negentropy-V8",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

Lineage

Qwen/Qwen3.5-9B
   │
   ├──── Darwin V7 evolutionary merge ──→ Darwin-9B-Opus
   │                                          │
   │                                       + NEG-Head + NEG-Gate (V8)
   │                                          │
   │                                          ▼
   │                                  FINAL-Bench/Darwin-9B-NEG  (Mother)
   │
   ├──── Trace Inversion SFT ──→ Jackrong/Negentropy-9B  (Father)
   │
   └──── Darwin V8 Gen4 FFN blend (α=0.85) ──→ THIS MODEL

Citation

Trace Inversion: Zhang, Morris, Shmatikov. How to Steal Reasoning Without Reasoning Traces. arXiv:2603.07267 (2026).
NEG: VIDRAFT internal Darwin V8 architecture.
Darwin V8 Gen4 engine: VIDRAFT evolutionary merge framework.

Built on 2026-05-10 by VIDRAFT (seawolf2357).