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
| """Merlin-Agent config: Qwen3_5 text config + quantum-injection fields.""" | |
| try: | |
| from transformers import Qwen3_5TextConfig | |
| except ImportError: # not exported at top level in some transformers versions | |
| from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig | |
| class MerlinAgentConfig(Qwen3_5TextConfig): | |
| model_type = "merlin_agent" | |
| def __init__( | |
| self, | |
| quantum_injection_alpha: float = 0.02, | |
| full_attention_layer_indices=(3, 7, 11, 15, 19, 23, 27, 31), | |
| proj_seed: int = 42, | |
| quantum_attestation=None, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.quantum_injection_alpha = quantum_injection_alpha | |
| self.full_attention_layer_indices = list(full_attention_layer_indices) | |
| self.proj_seed = proj_seed | |
| self.quantum_attestation = quantum_attestation or {} | |