Text Generation
Transformers
Safetensors
qwen3_5_text
merlin-agent
quantum-classical
quantum-kernel
ibm-quantum
otoc
quantum-provenance
merlin-research
code
conversational
Instructions to use Merlin-Research/Merlin-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "backend": "ibm_marrakesh", | |
| "ibm_job_id": "d92ve0t958jc73bsbong", | |
| "quantum_source": "SYK-scrambler OTOC on IBM Heron", | |
| "otoc_depths": [ | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5, | |
| 6 | |
| ], | |
| "n_qubits": 100, | |
| "shots": 2048, | |
| "tiles": 25, | |
| "collected_at": "2026-07-02T06:07:00.294531+00:00", | |
| "n_anchors": 64, | |
| "d": 6, | |
| "r": 6, | |
| "proj_seed": 42, | |
| "qfeat_sha256": "82c9c9e83a7b568c169cc229d8df801c4a2385a44c0efb4d95d1dbc7e00c6f9e", | |
| "A_sha256": "c33d3a6aee9293bf20f7a4ddc2d9fe5793dc8620233a8e9c2f04a548e8ddc268", | |
| "attestation_root": "9484dca40b66488a239fbbb12a9333a47458627f48b1c6d08d8241bf814caf48" | |
| } |