Instructions to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF",
	filename="L3.1-Celestial-Stone-2x8B-DPO.Q2_K.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 QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-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 QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-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/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-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/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-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 QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-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 QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3.1-Celestial-Stone-2x8B-DPO-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF

This is quantized version of v000000/L3.1-Celestial-Stone-2x8B-DPO created using llama.cpp

Original Model Card

Sampler:
Likes a low temperature due to the MoE architecture. I use 0.3 personally.

Llama-3.1-Celestial-Stone-2x8B-DPO (BF16)

DPO Trained, Mixture of Experts (14B).

Direct Preference Optimization run

----> Q6_K

L3.1-Celestial-Stone-2x8B Finetuned on Nvidia A100.

0.5 Epoch completed of dataset jondurbin/gutenberg-dpo-v0.1 with learning_rate=8e-6

Result seems pretty good. More compliant and verbose, less sloppy and safety aligned.

The first expert is Instruct 405B distillation/RP vector merge (Supernova-Lite, Niitama1.1, Storm)

The second expert is ERP/Reddit data merge (Celeste1.5, Stheno3.4, Storm)

The base model is Sao10k/L3.1-Stheno-3.4 with the Sunfall LoRa 0.6.1 to make it understand SillyTavern prompts and storywriting better.

Resultant merge finetuned on jondurbin/gutenberg-dpo-v0.1.

Prompt Template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>