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 7 days ago

Commit

f70f582

verified ·

1 Parent(s): d1368ef

Upload modeling_merlin_agent.py with huggingface_hub

Browse files

Files changed (1) hide show

modeling_merlin_agent.py +4 -1

modeling_merlin_agent.py CHANGED Viewed

@@ -8,7 +8,10 @@ __init__), so it survives save/reload — not a bare, non-persisted hook.
 alpha == 0 -> exact base model.
 """
 import torch
-from transformers import Qwen3_5ForCausalLM
 try:
     from .configuration_merlin_agent import MerlinAgentConfig

 alpha == 0 -> exact base model.
 """
 import torch
+try:
+    from transformers import Qwen3_5ForCausalLM
+except ImportError:  # not exported at top level in some transformers versions
+    from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5ForCausalLM
 try:
     from .configuration_merlin_agent import MerlinAgentConfig