Instructions to use Epiculous/Crimson_Dawn-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Epiculous/Crimson_Dawn-v0.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Epiculous/Crimson_Dawn-v0.2-GGUF",
	filename="Crimson_Dawn-v0.2_IQ1_S.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 Epiculous/Crimson_Dawn-v0.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Epiculous/Crimson_Dawn-v0.2-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 Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Epiculous/Crimson_Dawn-v0.2-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 Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Epiculous/Crimson_Dawn-v0.2-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 Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Epiculous/Crimson_Dawn-v0.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Epiculous/Crimson_Dawn-v0.2-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": "Epiculous/Crimson_Dawn-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M

Ollama
How to use Epiculous/Crimson_Dawn-v0.2-GGUF with Ollama:
```
ollama run hf.co/Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Epiculous/Crimson_Dawn-v0.2-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 Epiculous/Crimson_Dawn-v0.2-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 Epiculous/Crimson_Dawn-v0.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Epiculous/Crimson_Dawn-v0.2-GGUF to start chatting

Docker Model Runner
How to use Epiculous/Crimson_Dawn-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use Epiculous/Crimson_Dawn-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Epiculous/Crimson_Dawn-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Crimson_Dawn-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

Taking what seemed to work out well with Crimson_Dawn-v0.1, the new Crimson_Dawn-v0.2 is the same training methodology, training on Mistral-Nemo-Base-2407 this time I've added significantly more data, as well as trained using RSLoRA as opposed to regular LoRA. Another key change is training on ChatML as opposed to Mistral Formatting.

Quants!

full / exl2 / gguf

Prompting

The v0.2 models are trained on ChatML, the prompting structure goes a little something like this:

<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant

Context and Instruct

The v0.2 models are trained on ChatML, please use that Context and Instruct template.

Current Top Sampler Settings

Spicy_Temp
Violet_Twilight-Nitral-Special

Training

Training was done twice over 2 epochs each on two 2x NVIDIA A6000 GPUs using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct LoRA was applied to the modified base, resulting in what you see here.

Downloads last month: 108

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Datasets used to train Epiculous/Crimson_Dawn-v0.2-GGUF

Collection including Epiculous/Crimson_Dawn-v0.2-GGUF

Crimson Dawn

Collection

Crimson Dawn model line • 6 items • Updated Oct 16, 2024 • 2