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
```

squ11z1 commited on 3 days ago

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@@ -43,10 +43,6 @@ On the harder **SWE-bench Pro** leaderboard it sits mid-pack among current front
 ![swe-bench-pro](assets/swe_bench_pro.png)
-Full inherited coding suite (norm-controlled merge):
-![benchmarks](assets/benchmarks.png)
 | Benchmark | Score |
 |---|---|
 | SWE-bench Verified | 69.4 |
@@ -232,7 +228,7 @@ Quantized builds: [`Merlin-Research/Merlin-Agent-GGUF`](https://huggingface.co/M
 ```bibtex
 @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 job d92ve0t958jc73bsbong (ibm\_marrakesh);

 ![swe-bench-pro](assets/swe_bench_pro.png)
 | Benchmark | Score |
 |---|---|
 | SWE-bench Verified | 69.4 |
 ```bibtex
 @misc{merlinresearch2026merlinagent,
   title        = {Merlin-Agent: A Quantum-Classical Coding Model with Heron-Baked Weights},
+  author       = {Shushman, Mykhailo / Synolyts, Oleksandr},
   institution  = {Merlin Research AB},
   year         = {2026},
   note         = {IBM Heron job d92ve0t958jc73bsbong (ibm\_marrakesh);