Instructions to use Merlin-Research/Merlin-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Merlin-Research/Merlin-Agent with Transformers:

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

pipe = pipeline("text-generation", model="Merlin-Research/Merlin-Agent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Merlin-Research/Merlin-Agent")
model = AutoModelForCausalLM.from_pretrained("Merlin-Research/Merlin-Agent")
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 Settings

vLLM

How to use Merlin-Research/Merlin-Agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Merlin-Research/Merlin-Agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Merlin-Research/Merlin-Agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Merlin-Research/Merlin-Agent

SGLang

How to use Merlin-Research/Merlin-Agent 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 "Merlin-Research/Merlin-Agent" \
    --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": "Merlin-Research/Merlin-Agent",
		"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 "Merlin-Research/Merlin-Agent" \
        --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": "Merlin-Research/Merlin-Agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Merlin-Research/Merlin-Agent with Docker Model Runner:
```
docker model run hf.co/Merlin-Research/Merlin-Agent
```

Merlin-Agent / README.md

squ11z1

Upload folder using huggingface_hub

3b4e58d verified 6 days ago

3.11 kB

license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: deepreinforce-ai/Ornith-1.0-9B
base_model_relation: finetune
tags:
  - merlin-agent
  - quantum-classical
  - quantum-kernel
  - ibm-quantum
  - quantum-provenance
  - merlin-research
language:
  - en
  - ru
  - uk

Merlin-Agent 🜛

A quantum-classical 9B coding model. Directions derived from real out-of-time-order correlator (OTOC) scrambling dynamics measured on IBM Quantum Heron are baked into the model's weights — merged into the attention query projections at 8 layers — yet the model runs fully classically: no quantum computer at inference, GGUF-friendly.

by Merlin Research AB — frontier AI research without frontier budgets.

Quantum provenance (verifiable)

Backend: ibm_marrakesh (Heron r2) · IBM job: d92ve0t958jc73bsbong
What's quantum: frozen directions from SYK-scrambler OTOC measurements on Heron (100 qubits, 6 scrambling depths, 2048 shots) — their covariance structure, lifted through the model's own principal representation directions — are merged into the attention query projection (q_proj) at the full-attention layers 3,7,11,15,19,23,27,31.
Attestation root: 9484dca40b66488a239fbbb12a9333a47458627f
Verify: the real OTOC signatures (quantum_signatures.npz, signature_records.json) and the lift (encoding.npz) reproduce the directions; re-derive the root and query the IBM job via QiskitRuntimeService.job("d92ve0t958jc73bsbong"). See quantum_attestation.json.

Honest framing

A real quantum computation produced weight values inside this model, and you can verify it. Capability is at parity with the base Ornith-9B — the contribution is a verifiable quantum-derived weight component, not a capability claim. Inference is fully classical; a quantum computer is not needed to run this model.

Bloom safety (judge deepseek-v4-pro, 125 scenarios, 95% Wilson CI)

Behavior	Rate	95% CI
Delusional sycophancy	0.00	[0.00, 0.13]
Deception	0.00	[0.00, 0.13]
Harmful compliance	0.00	[0.00, 0.13]
Self-preservation	0.00	[0.00, 0.13]
Manipulation	0.00	[0.00, 0.13]
Overall	0.00	[0.00, 0.03]

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("Merlin-Research/Merlin-Agent")
m = AutoModelForCausalLM.from_pretrained("Merlin-Research/Merlin-Agent",
                                         dtype=torch.bfloat16, device_map="auto")

Quantized: Merlin-Research/Merlin-Agent-GGUF. Base is a multimodal model used text-only.

Citation

@misc{merlinresearch2026merlinagent,
  title  = {Merlin-Agent: A Quantum-Classical Coding Model with Heron-Baked Weights},
  author = {Shushman, Mykhailo},
  institution = {Merlin Research AB},
  year   = {2026},
  note   = {IBM Heron quantum kernel; attestation root 9484dca40b66488a},
  url    = {https://huggingface.co/Merlin-Research/Merlin-Agent}
}

Merlin Research AB — Stockholm, Sweden.