Instructions to use FINAL-Bench/Darwin-36B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-36B-Opus with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-36B-Opus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-36B-Opus")
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Darwin-36B-Opus")
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

vLLM

How to use FINAL-Bench/Darwin-36B-Opus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-36B-Opus"
# 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-36B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-36B-Opus

SGLang

How to use FINAL-Bench/Darwin-36B-Opus 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-36B-Opus" \
    --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-36B-Opus",
		"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-36B-Opus" \
        --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-36B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-36B-Opus with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-36B-Opus
```

fyi

by jukefr - opened 1 day ago

Discussion

jukefr

1 day ago

•

edited 1 day ago

sample on this subset of term-bench2.0 tasks was already enough to me feel free to bench more if you want, tested with pi-agent

  ┌───────────────────────────┬─────────────┬────────────┬───────┬───────┬───────┬───────┐
  │           Task            │   Qwen3.6   │   Darwin   │ Q dur │ D dur │ Q out │ D out │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ fix-git                   │         3/3 │        2/3 │   41s │   31s │  2.3K │  1.6K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ prove-plus-comm           │         2/3 │        2/3 │  377s │   36s │   11K │  1.9K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ cobol-modernization       │         1/3 │        2/3 │  439s │  215s │   26K │   13K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ overfull-hbox             │         0/3 │        0/3 │  484s │  103s │   29K │  5.9K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ break-filter-js-from-html │         0/3 │        0/3 │  297s │  275s │   18K │   16K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ filter-js-from-html       │         0/3 │        0/3 │   80s │  671s │  4.6K │   33K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ kv-store-grpc             │         2/3 │        0/3 │   34s │   42s │  1.3K │  1.8K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ multi-source-data-merger  │         3/3 │        1/3 │   64s │   98s │  3.5K │  5.8K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ regex-log                 │         1/3 │        0/3 │  461s │  580s │   28K │   34K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ git-leak-recovery         │         2/3 │        1/3 │   35s │   39s │  1.8K │  1.9K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ pypi-server               │         0/3 │        0/3 │   23s │   46s │  0.9K │  2.5K │
  ├───────────────────────────┼─────────────┼────────────┼───────┼───────┼───────┼───────┤
  │ TOTAL                     │ 14/33 (42%) │ 8/33 (24%) │       │       │       │       │
  └───────────────────────────┴─────────────┴────────────┴───────┴───────┴───────┴───────┘

SeaWolf-AI

FINAL_Bench org about 19 hours ago

Thanks for running the benchmark and sharing the numbers.Quick note on positioning: Darwin-36B-Opus is published as a reasoning-focused evolutionary merge (GPQA Diamond 88.4%, tying Qwen3.5-397B-A17B), not as an agentic coder. The Darwin Opus line is bred for graduate-level scientific reasoning — physics, chemistry, biology Q&A in the GPQA style — and is not tuned for terminal/agent workflows. For agent and coding tasks we'd recommend the Qwen Coder line.Two observations on your runs that may explain part of the gap:
System prompt: Darwin needs enable_thinking=true via the Qwen chat template, and the agent harness needs to leave room for the ... block before tool calls. If pi-agent strips or truncates the thinking trace, Darwin loses most of its reasoning lift. You can confirm in the output — if you don't see a block, the harness is filtering it.

Output token compactness is by design: Darwin Opus inherits a Father with 75% Gated-DeltaNet + 25% Gated-Attention. Post-thinking responses are deliberately compressed (FFN α asymmetry from the merge genome), which is the opposite of what agent benchmarks reward — they reward verbose step-by-step tool chains. That's a known trade-off for this checkpoint, not a regression.
We'd be very interested to see your numbers on the same subset with (a) enable_thinking=true set in the request, and (b) the agent template that preserves the thinking trace. Happy to help if there's a specific task where you'd like to dig in.For full context: the Darwin Family methodology is currently under peer review at ARR May 2026 (training-free reasoning scaling) — coding/agent performance is explicitly out of scope of that submission.

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