Instructions to use QuantFactory/Violet_Twilight-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Violet_Twilight-v0.1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Violet_Twilight-v0.1-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 QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Violet_Twilight-v0.1-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 QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Violet_Twilight-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Violet_Twilight-v0.1-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": "QuantFactory/Violet_Twilight-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Violet_Twilight-v0.1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Violet_Twilight-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Violet_Twilight-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Violet_Twilight-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Violet_Twilight-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Violet_Twilight-v0.1-GGUF

This is quantized version of Epiculous/Violet_Twilight-v0.1 created using llama.cpp

Original Model Card

Now for something a bit different, Violet_Twilight! This model is a SLERP merge of Azure_Dusk and Crimson_Dawn!

Quants!

full / exl2 / gguf

Prompting

Violet_Twilight's models were trained with the Mistral Instruct template, therefore it should be prompted in a similar way that you would prompt any other mistral based model.

"<s>[INST] Prompt goes here [/INST]<\s>"

Context and Instruct

Magnum-123B-Context.json
Magnum-123B-Instruct.json
*** NOTE ***
There have been reports of the quantized model misbehaving with the mistral prompt, if you are seeing issues it may be worth trying ChatML Context and Instruct templates. If you are using GGUF I strongly advise using ChatML, for some reason that quantization performs better using ChatML.

Current Top Sampler Settings

Violet_Twilight-Nitral-Special- Considered the best settings!
Crimson_Dawn-Nitral-Special
Crimson_Dawn-Magnum-Style

Tokenizer

If you are using SillyTavern, please set the tokenizer to API (WebUI/ koboldcpp)

Merging

The following config was used to merge Azure Dusk and Crimson Dawn

slices:
  - sources:
      - model: Epiculous/Azure_Dusk-v0.1
        layer_range: [0, 40]
      - model: Epiculous/Crimson_Dawn-V0.1
        layer_range: [0, 40]
merge_method: slerp
base_model: Epiculous/Azure_Dusk-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

Downloads last month: 154

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit