Instructions to use QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-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/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-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/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-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-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-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-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc-GGUF

This is quantized version of Casual-Autopsy/L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc created using llama.cpp

Original Model Card

L3-bluuwhale-SAO-MIX-8B-V1_fp32-merge-calc

This is a remerge of bluuwhale's merger using the exact yaml config with the only difference being that merge calculations are done in fp32 instead of bfp16

I've done this since I'm planning to use this for another merger, but you can use as is if you wish.

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:

Configuration

The following YAML configuration was used to produce this model:

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
base_model: Sao10K/L3-8B-Niitama-v1
merge_method: della
dtype: float32
out_dtype: bfloat16