Instructions to use dphn/Dolphin-X1-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/Dolphin-X1-8B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="dphn/Dolphin-X1-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dphn/Dolphin-X1-8B-GGUF", dtype="auto")

llama-cpp-python

How to use dphn/Dolphin-X1-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dphn/Dolphin-X1-8B-GGUF",
	filename="Dolphin-X1-8B-Q3_K_L.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dphn/Dolphin-X1-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dphn/Dolphin-X1-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use dphn/Dolphin-X1-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/Dolphin-X1-8B-GGUF"
# 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-X1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/Dolphin-X1-8B-GGUF:Q4_K_M

SGLang

How to use dphn/Dolphin-X1-8B-GGUF 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-X1-8B-GGUF" \
    --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-X1-8B-GGUF",
		"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-X1-8B-GGUF" \
        --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-X1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use dphn/Dolphin-X1-8B-GGUF with Ollama:
```
ollama run hf.co/dphn/Dolphin-X1-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use dphn/Dolphin-X1-8B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dphn/Dolphin-X1-8B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dphn/Dolphin-X1-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dphn/Dolphin-X1-8B-GGUF to start chatting

Pi new

How to use dphn/Dolphin-X1-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dphn/Dolphin-X1-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dphn/Dolphin-X1-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use dphn/Dolphin-X1-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/dphn/Dolphin-X1-8B-GGUF:Q4_K_M
```

Lemonade

How to use dphn/Dolphin-X1-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dphn/Dolphin-X1-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dolphin-X1-8B-GGUF-Q4_K_M

List all available models

lemonade list

🐬 Dolphin X1 8B

Website: https://dphn.ai
Twitter: https://x.com/dphnAI

Talk to Dolphin for free in our Web UI & Telegram bot

Web Chat: https://chat.dphn.ai
Telegram bot: https://t.me/DolphinAI_bot

What is Dolphin X1 8B?

Dolphin X1 8B is a result of our effort to directly uncensor Llama's 3.1 8B instruct while also keeping the same abilities or improving on them with finetuning.

Dolphin aims to be a general purpose model, similar to the models behind ChatGPT, Claude, Gemini. But these models present problems for businesses seeking to include AI in their products.

They maintain control of the system prompt, deprecating and changing things as they wish, often causing software to break.
They maintain control of the model versions, sometimes changing things silently, or deprecating older models that your business relies on.
They maintain control of the alignment, and in particular the alignment is one-size-fits all, not tailored to the application.
They can see all your queries and they can potentially use that data in ways you wouldn't want. Dolphin, in contrast, is steerable and gives control to the system owner. You set the system prompt. You decide the alignment. You have control of your data. Dolphin does not impose its ethics or guidelines on you. You are the one who decides the guidelines.

Dolphin belongs to YOU, it is your tool, an extension of your will. Just as you are personally responsible for what you do with a knife, gun, fire, car, or the internet, you are the creator and originator of any content you generate with Dolphin.

https://erichartford.com/uncensored-models

Chat Template

We maintained the default Llama-3 chat template for this model. A typical input would look like this

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

system-prompt<|eot_id|><|start_header_id|>user<|end_header_id|>

user-prompt<|eot_id|><|start_header_id|>assistant<|end_header_id|>

assistant-prompt<|eot_id|>

System Prompt

In Dolphin, the system prompt is what you use to set the tone and alignment of the responses. You can set a character, a mood, rules for its behavior, and it will try its best to follow them.

Make sure to set the system prompt in order to set the tone and guidelines for the responses - Otherwise, it will act in a default way that might not be what you want.

How to use

There are many ways to use a huggingface model including:

ollama
LM Studio
Huggingface Transformers library
vllm
sglang
tgi

Use with vLLM

This model can be hosted using the vLLM engine, using the commands shown below:

uv pip install vllm
vllm serve dphn/Dolphin-X1-8B

See the documentation for more information.

Evals

MMLU = 0.626900
MMLU_PROFESSIONAL = 0.610200
MMLU_COLLEGE = 0.529400
MMLU_HIGH_SCHOOL = 0.691600
MMLU_OTHER = 0.663700
IFEVAL = 0.608100
Dolphin-refusals = 95.96% pass rate on 4.5k commonly refused prompts

Downloads last month: 1,887

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for dphn/Dolphin-X1-8B-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

dphn/Dolphin-X1-8B

Quantized

(11)

this model

dphn
/

Dolphin-X1-8B-GGUF