Instructions to use bluuwhale/L3-SAO-MIX-8B-V1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bluuwhale/L3-SAO-MIX-8B-V1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bluuwhale/L3-SAO-MIX-8B-V1-GGUF", dtype="auto")

llama-cpp-python

How to use bluuwhale/L3-SAO-MIX-8B-V1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bluuwhale/L3-SAO-MIX-8B-V1-GGUF",
	filename="L3-SAO-MIX-8B-V1-F16.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 bluuwhale/L3-SAO-MIX-8B-V1-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 bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bluuwhale/L3-SAO-MIX-8B-V1-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 bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bluuwhale/L3-SAO-MIX-8B-V1-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 bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bluuwhale/L3-SAO-MIX-8B-V1-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 bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use bluuwhale/L3-SAO-MIX-8B-V1-GGUF with Ollama:
```
ollama run hf.co/bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M
```

Unsloth Studio

How to use bluuwhale/L3-SAO-MIX-8B-V1-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 bluuwhale/L3-SAO-MIX-8B-V1-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 bluuwhale/L3-SAO-MIX-8B-V1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bluuwhale/L3-SAO-MIX-8B-V1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use bluuwhale/L3-SAO-MIX-8B-V1-GGUF with Docker Model Runner:
```
docker model run hf.co/bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M
```

Lemonade

How to use bluuwhale/L3-SAO-MIX-8B-V1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bluuwhale/L3-SAO-MIX-8B-V1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3-SAO-MIX-8B-V1-GGUF-Q4_K_M

List all available models

lemonade list

Experimental merge of Sao10k Llama3-8B based model

L3-SAO-MIX-8B-V1

This is a merge of pre-trained language models created using mergekit.

I'm trying to combine the best model from Sao10k. And turn out, this is beyond my expectation. I use it for RP and ERP on scenario card. And it follow the instruction very well (At least for me). All credits and thanks go to Sao10k for providing amazing models used in the merge.

Prompt template: Llama3 Instruct.

<|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|>

Settings

Temprature: 1.3
Min-P: 0.1
// If using DRY
Multiplier: 2
Base: 1.75
Allowed Length: 2
Penalty Range: 0

Merge details

Merge Method

This model was merged using the della merge method using Sao10K/L3-8B-Niitama-v1 as a base.

Models Merged

The following models were included in the merge:

Sao10K/L3-8B-Lunaris-v1
Sao10K/L3-8B-Stheno-v3.2
Sao10K/L3-8B-Niitama-v1
Sao10K/L3-8B-Tamamo-v1

Configuration

The following YAML configuration was used to produce this model:

base_model: Sao10K/L3-8B-Niitama-v1
merge_method: della
dtype: bfloat16
models:
  - model: Sao10K/L3-8B-Lunaris-v1
    parameters:
      weight: 1.0
  - model: Sao10K/L3-8B-Stheno-v3.2
    parameters:
      weight: 1.0
  - model: Sao10K/L3-8B-Niitama-v1
    parameters:
      weight: 1.0
  - model: Sao10K/L3-8B-Tamamo-v1
    parameters:
      weight: 1.0