Instructions to use InferenceIllusionist/Magic-Dolphin-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InferenceIllusionist/Magic-Dolphin-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="InferenceIllusionist/Magic-Dolphin-7b-GGUF",
	filename="Magic-Dolphin-7b-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use InferenceIllusionist/Magic-Dolphin-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf InferenceIllusionist/Magic-Dolphin-7b-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 InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf InferenceIllusionist/Magic-Dolphin-7b-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 InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf InferenceIllusionist/Magic-Dolphin-7b-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 InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use InferenceIllusionist/Magic-Dolphin-7b-GGUF with Ollama:
```
ollama run hf.co/InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use InferenceIllusionist/Magic-Dolphin-7b-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 InferenceIllusionist/Magic-Dolphin-7b-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 InferenceIllusionist/Magic-Dolphin-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for InferenceIllusionist/Magic-Dolphin-7b-GGUF to start chatting

Docker Model Runner
How to use InferenceIllusionist/Magic-Dolphin-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M
```

Lemonade

How to use InferenceIllusionist/Magic-Dolphin-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull InferenceIllusionist/Magic-Dolphin-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Magic-Dolphin-7b-GGUF-Q4_K_M

List all available models

lemonade list

GGUF files for Magic-Dolphin-7b

Magic-Dolphin-7b

For fp16 files please look here

A linear merge of:

These three models showed excellent acumen in technical topics so I wanted to see how they would behave together in a merge. Several different ratios were tested before this release, in the end a higher weighting for merlinite-7b helped smooth out some edges. This model is a test of how LAB tuning is impacted by merges with models leveraging DPO.

Benchmark Performance

Name	Avg.	ARC	HellaSwag	MMLU	TruthfulQA	Winograde	GSM8K
Magic-Dolphin-7b	67.48	65.78	85.61	64.64	58.01	79.64	51.18
dolphin-2.6-mistral-7b-dpo-laser	67.28	66.3	85.73	63.16	61.71	79.16	47.61
merlinite-7b	N/A	63.99	84.37	64.88	N/A	78.24	N/A
Hyperion-1.5-Mistral-7B	61.43	60.49	83.64	63.57	41.78	78.61	40.49

This was my first experiment with merging models so any feedback is greatly appreciated.

Uses Alpaca template.

Sample Question

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: models/dolphin-2.6-mistral-7b-dpo-laser
    parameters:
      weight: 1.0
  - model: models/Hyperion-1.5-Mistral-7B
    parameters:
      weight: 0.3
  - model: models/merlinite-7b
    parameters:
      weight: 0.5
merge_method: linear
dtype: float16

Downloads last month: 32

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

Inference Providers NEW

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Collection including InferenceIllusionist/Magic-Dolphin-7b-GGUF

GGUFs

Collection

I take requests, feel free to drop me a line in the community posts • 31 items • Updated Apr 28 • 3

Paper for InferenceIllusionist/Magic-Dolphin-7b-GGUF

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8