Instructions to use eye1patch/merlin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eye1patch/merlin with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="eye1patch/merlin")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("eye1patch/merlin", dtype="auto")

Merlin

How to use eye1patch/merlin with Merlin:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eye1patch/merlin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eye1patch/merlin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eye1patch/merlin",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eye1patch/merlin

SGLang

How to use eye1patch/merlin 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 "eye1patch/merlin" \
    --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": "eye1patch/merlin",
		"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 "eye1patch/merlin" \
        --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": "eye1patch/merlin",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eye1patch/merlin with Docker Model Runner:
```
docker model run hf.co/eye1patch/merlin
```

eye1patch commited on 20 days ago

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@@ -23,7 +23,7 @@ This is the model card draft for the MERLIN Hugging Face model repository. It ca
 - Paper: [MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals](https://arxiv.org/abs/2603.08174)
 - Project page: [https://em-merlin.github.io/](https://em-merlin.github.io/)
-- Code: `https://github.com/em-merlin/MERLIN`
 - Training dataset: [eye1patch/EM-134K](https://huggingface.co/datasets/eye1patch/EM-134K)
 - Evaluation benchmark: [eye1patch/EM-Bench](https://huggingface.co/datasets/eye1patch/EM-Bench)

 - Paper: [MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals](https://arxiv.org/abs/2603.08174)
 - Project page: [https://em-merlin.github.io/](https://em-merlin.github.io/)
+- Code: [https://github.com/eye1patch/merlin/](https://github.com/eye1patch/merlin/)
 - Training dataset: [eye1patch/EM-134K](https://huggingface.co/datasets/eye1patch/EM-134K)
 - Evaluation benchmark: [eye1patch/EM-Bench](https://huggingface.co/datasets/eye1patch/EM-Bench)