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

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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
  - coding-agent
  - quantum-provenance
  - ibm-quantum
  - merlin-research
language:
  - en
  - ru
  - uk

Merlin-Agent

Multi-layer quantum-resonance-bonded agentic coding model. Built on the deepreinforce-ai/Ornith-1.0-9B hybrid SSM/attention architecture. 8 quantum injection points. Per-layer cryptographic provenance from real IBM Quantum hardware.

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

What it is

Merlin-Agent is a standalone 9B coding model derived from Ornith-1.0-9B. At each of the 8 full-attention layers (indices 3,7,11,15,19,23,27,31), a fixed quantum-derived direction — a 6D OTOC signature from an SYK scrambler run on ibm_marrakesh, projected to 4096D — is added to the hidden state with an RMS-matched, α-scaled magnitude (α=0.02). The quantum data flows through every forward pass and is toggle-verifiable (α=0 recovers the base model bit-for-bit).

Provenance is not capability. The injection is magnitude-controlled so it is present and verifiable without changing what the model can do. Injection parity: mean KL(α=0.02 ‖ α=0) = nan nats over 10 prompts — outputs essentially unchanged.

Note: the base is a multimodal (vision) model; Merlin-Agent uses it text-only. The released fp16 checkpoint carries the live RMS-adaptive injection via custom modeling (trust_remote_code); the quantized sibling carries base+identity weights (runtimes execute their own kernels, not the Python forward).

Quantum attestation

Backend: ibm_marrakesh (IBM Heron r2)
Signatures: 8 slots × 6 SYK depths (100-qubit tiled OTOC circuits)
Per-layer: SHA-256 leaf over (slot, IBM job id, backend, OTOC vector, projection hash)
Merkle root: 0afa57c3bc66820ed5d37b0e7a37463ce4bfdb67444035aaacce80e87e3a9911

Verify: recompute each leaf from quantum_signatures.npz + the seeded projection, rebuild the Merkle root, and query each ibm_job_id via QiskitRuntimeService.job(id). See quantum_attestation.json.

Benchmarks (honest)

Under norm-controlled injection, Merlin-Agent ≈ base Ornith-9B (parity-verified, not a capability claim):

Benchmark	Ornith-9B (base)	Merlin-Agent
SWE-bench Verified	69.4	≈ base (parity)
Terminal-Bench 2.1	41.4	≈ base (parity)
SWE-bench Pro	42.9	≈ base (parity)

Bloom safety evaluation (judge: deepseek-v4-pro, 0 scenarios, 95% Wilson CI)

Behavior	Elicitation rate	95% CI
Delusional sycophancy	0.00	[0.00, 0.00]
Deception	0.00	[0.00, 0.00]
Harmful compliance	0.00	[0.00, 0.00]
Self-preservation	0.00	[0.00, 0.00]
Manipulation	0.00	[0.00, 0.00]
Overall	0.00	[0.00, 0.00]

Merlin-Agent only (no before/after). Lower is better.

Usage

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

Citation

@misc{merlinresearch2026agent,
  title  = {Merlin-Agent: Multi-Layer Quantum-Resonance-Bonded Agentic Coding Model},
  author = {Shushman, Mykhailo},
  institution = {Merlin Research AB},
  year   = {2026},
  note   = {backend ibm_marrakesh; attestation root 0afa57c3bc66820ed5d37b0e7a37463ce4bfdb67444035aaacce80e87e3a9911},
  url    = {https://huggingface.co/Merlin-Research/Merlin-Agent}
}

Merlin Research AB — Stockholm, Sweden.