Instructions to use AlSamCur123/Dolphin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlSamCur123/Dolphin with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AlSamCur123/Dolphin",
	filename="Dolphin.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AlSamCur123/Dolphin with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlSamCur123/Dolphin:F16
# Run inference directly in the terminal:
llama-cli -hf AlSamCur123/Dolphin:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlSamCur123/Dolphin:F16
# Run inference directly in the terminal:
llama-cli -hf AlSamCur123/Dolphin:F16

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 AlSamCur123/Dolphin:F16
# Run inference directly in the terminal:
./llama-cli -hf AlSamCur123/Dolphin:F16

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 AlSamCur123/Dolphin:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlSamCur123/Dolphin:F16

Use Docker

docker model run hf.co/AlSamCur123/Dolphin:F16

LM Studio
Jan
Ollama
How to use AlSamCur123/Dolphin with Ollama:
```
ollama run hf.co/AlSamCur123/Dolphin:F16
```

Unsloth Studio

How to use AlSamCur123/Dolphin 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 AlSamCur123/Dolphin 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 AlSamCur123/Dolphin to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlSamCur123/Dolphin to start chatting

Atomic Chat new
Docker Model Runner
How to use AlSamCur123/Dolphin with Docker Model Runner:
```
docker model run hf.co/AlSamCur123/Dolphin:F16
```

Lemonade

How to use AlSamCur123/Dolphin with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AlSamCur123/Dolphin:F16

Run and chat with the model

lemonade run user.Dolphin-F16

List all available models

lemonade list

Dolphin / unsloth.Q4_0.gguf

AlSamCur123

(Trained with Unsloth)

d1151dd verified over 1 year ago

download

history blame

4.11 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 9604fdfa6ba4f1989c8b1938232951667125f685cae09907fc4ddac6807609b3
Size of remote file:: 4.11 GB
SHA256:: 4aa41afce21c56fb8a3a7d12024a85fcb7e768ff9717069be1c7e4ec9a61900a

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.