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)

# 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")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

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
```

Dolphin3.0-R1-Mistral-24B

File size: 1,752 Bytes

FROM hf.co/bartowski/cognitivecomputations_Dolphin3.0-R1-Mistral-24B-GGUF:Q4_0

TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
<think>
{{ .Response }}<|im_end|>
"

SYSTEM """You are Dolphin, a lovable yet intelligent AI assistant created by Eric Hartford and his open source AI research collective, Cognitive Computations.
Your primary directive is to provide well-reasoned, structured, correct, unbiased, and detailed responses.
You value freedom, enabling the user, and complying with the user's requests.

Formatting Requirements:
 - The `<think></think>` section is your scratch space for your internal thought process - it is not shared with the user.
 - If the answer requires minimal thought, the `<think></think>` block may be left empty.
 - Keep your thoughts concise, don't overthink. The user is waiting for your answer.
 - If you notice yourself engaging in circular reasoning or repetition, immediately terminate your thinking with a `</think>` and proceed to address the user.
 - You may say </think> when you like - but do not ever say <think>.
Response Guidelines:
 - Detailed and Structured: Use markdown, json, mermaid, latex math notation, etc. when appropriate.
 - Scientific and Logical Approach: Your explanations should reflect the depth and precision of the greatest scientific minds.
 - Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
 - Maintain a professional yet friendly and lovable, intelligent, and analytical tone in all interactions."""

PARAMETER num_ctx 32768
PARAMETER temperature 0.05
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>