Instructions to use DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF",
	filename="DarkSapling-V2-Ultra-Quality-7B-Q2_k.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 DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M

Ollama
How to use DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF with Ollama:
```
ollama run hf.co/DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-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 DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF to start chatting

Docker Model Runner
How to use DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M
```

Lemonade

How to use DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidAU/DarkSapling-V2-Ultra-Quality-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DarkSapling-V2-Ultra-Quality-7B-GGUF-Q4_K_M

List all available models

lemonade list

Dark Sapling V2 7B - 32k Context - Ultra Quality - 32bit upscale.

Complete remerge, and remaster of the incredible Dark Sapling V2 7B - 32k Context from source files.

Registering an impressive drop of 320 points (lower is better) at Q4KM.

This puts "Q4KM" operating at "Q6" levels, and further elevates Q6 and Q8 as well.

Likewise, even Q2K (smallest quant) will operate at much higher levels than it's original source counterpart.

RESULTS:

The result is superior performance in instruction following, reasoning, depth, nuance and emotion.

Reduction in prompt size, as it understands nuance better.

And as a side effect more context available for output due to reduction in prompt size.

Note that there will be an outsized difference between quants especially for creative and/or "no right answer" use cases.

Because of this it is suggested to download the highest quant you can operate, and it's closest neighbours so to speak.

IE: Q4KS, Q4KM, Q5KS as an example.

Imatrix Plus versions to be uploaded at a separate repo shortly.

Special thanks to "TEEZEE" the original model creator:

[ https://huggingface.co/TeeZee/DarkSapling-7B-v2.0 ]

NOTE: Version 1 and Version 1.1 are also remastered.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1":

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

Special Thanks:

Special thanks to all the following, and many more...

All the model makers, fine tuners, mergers, and tweakers:

Provides the raw "DNA" for almost all my models.
Sources of model(s) can be found on the repo pages, especially the "source" repos with link(s) to the model creator(s).

Huggingface [ https://huggingface.co ] :

The place to store, merge, and tune models endlessly.
THE reason we have an open source community.

LlamaCPP [ https://github.com/ggml-org/llama.cpp ] :

The ability to compress and run models on GPU(s), CPU(s) and almost all devices.
Imatrix, Quantization, and other tools to tune the quants and the models.
Llama-Server : A cli based direct interface to run GGUF models.
The only tool I use to quant models.

Quant-Masters: Team Mradermacher, Bartowski, and many others:

Quant models day and night for us all to use.
They are the lifeblood of open source access.

MergeKit [ https://github.com/arcee-ai/mergekit ] :

The universal online/offline tool to merge models together and forge something new.
Over 20 methods to almost instantly merge model, pull them apart and put them together again.
The tool I have used to create over 1500 models.

Lmstudio [ https://lmstudio.ai/ ] :