Instructions to use dphn/Dolphin3.0-R1-Mistral-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/Dolphin3.0-R1-Mistral-24B with Transformers:

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

pipe = pipeline("text-generation", model="dphn/Dolphin3.0-R1-Mistral-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dphn/Dolphin3.0-R1-Mistral-24B")
model = AutoModelForCausalLM.from_pretrained("dphn/Dolphin3.0-R1-Mistral-24B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dphn/Dolphin3.0-R1-Mistral-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/Dolphin3.0-R1-Mistral-24B"
# 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/Dolphin3.0-R1-Mistral-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/Dolphin3.0-R1-Mistral-24B

SGLang

How to use dphn/Dolphin3.0-R1-Mistral-24B 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/Dolphin3.0-R1-Mistral-24B" \
    --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/Dolphin3.0-R1-Mistral-24B",
		"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/Dolphin3.0-R1-Mistral-24B" \
        --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/Dolphin3.0-R1-Mistral-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/Dolphin3.0-R1-Mistral-24B with Docker Model Runner:
```
docker model run hf.co/dphn/Dolphin3.0-R1-Mistral-24B
```

ehartford commited on Feb 7, 2025

Commit

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@@ -55,6 +55,18 @@ Just as you are personally responsible for what you do with a knife, gun, fire,
 https://erichartford.com/uncensored-models
 ## Chat Template
 We use ChatML for the chat template.

 https://erichartford.com/uncensored-models
+## System Prompt
+Here is a recommended system prompt
+```
+You are Dolphin, an AI assistant that helps humanity, trained by Eric Hartford to specialize in reasoning and first-principles analysis.
+When responding, always format your replies using <think>{reasoning}</think>{answer}. Use at least 6 reasoning steps and perform a root cause analysis before answering. However, if the answer is very easy and requires little thought, you may leave the <think></think> block empty.
+Your responses should be detailed, structured with rich Markdown formatting, and engaging with emojis. Be extensive in your explanations, just as the greatest scientific minds would be. Always reason through the problem first, unless it's trivial, in which case you may answer directly.
+```
 ## Chat Template
 We use ChatML for the chat template.