Instructions to use dphn/Dolphin-X1-Trinity-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/Dolphin-X1-Trinity-Nano with Transformers:

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

pipe = pipeline("text-generation", model="dphn/Dolphin-X1-Trinity-Nano", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dphn/Dolphin-X1-Trinity-Nano", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("dphn/Dolphin-X1-Trinity-Nano", trust_remote_code=True, device_map="auto")
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 dphn/Dolphin-X1-Trinity-Nano with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/Dolphin-X1-Trinity-Nano"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dphn/Dolphin-X1-Trinity-Nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/Dolphin-X1-Trinity-Nano

SGLang

How to use dphn/Dolphin-X1-Trinity-Nano 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 "dphn/Dolphin-X1-Trinity-Nano" \
    --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": "dphn/Dolphin-X1-Trinity-Nano",
		"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 "dphn/Dolphin-X1-Trinity-Nano" \
        --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": "dphn/Dolphin-X1-Trinity-Nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/Dolphin-X1-Trinity-Nano with Docker Model Runner:
```
docker model run hf.co/dphn/Dolphin-X1-Trinity-Nano
```

dphnAI commited on May 29

Commit

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@@ -18,6 +18,7 @@ language:
 - zh
 ---
 # 🐬 Dolphin X1 Trinity Nano
 Website: https://dphn.ai
@@ -27,13 +28,11 @@ Web Chat: https://chat.dphn.ai
 ![image](https://cdn-uploads.huggingface.co/production/uploads/68485b28c949339ca04c370c/Unxst5YH2E83fSw-Ki2OU.jpeg)
 ## Overview
 Dolphin X1 Trinity Nano directly decensors Arcee AI's Trinity Nano model while maintaining instruction-following capabilities. This model is the first trained entirely using our new RL environments aimed to de-align models and achieves lower refusal rates than other models in its class without compromising performance.
-Details on the environment can be found here: <link>
 ## Sponsors

 - zh
 ---
 # 🐬 Dolphin X1 Trinity Nano
 Website: https://dphn.ai
 ![image](https://cdn-uploads.huggingface.co/production/uploads/68485b28c949339ca04c370c/Unxst5YH2E83fSw-Ki2OU.jpeg)
 ## Overview
 Dolphin X1 Trinity Nano directly decensors Arcee AI's Trinity Nano model while maintaining instruction-following capabilities. This model is the first trained entirely using our new RL environments aimed to de-align models and achieves lower refusal rates than other models in its class without compromising performance.
+Details on the environment can be found here: [https://blog.dphn.ai/dphn-x1-trinity-nano/](https://blog.dphn.ai/dphn-x1-trinity-nano/)
 ## Sponsors