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

Libraries

How to use QuantFactory/SAINEMO-reMIX-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/SAINEMO-reMIX-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/SAINEMO-reMIX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/SAINEMO-reMIX-GGUF",
	filename="SAINEMO-reMIX.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/SAINEMO-reMIX-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/SAINEMO-reMIX-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/SAINEMO-reMIX-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/SAINEMO-reMIX-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/SAINEMO-reMIX-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/SAINEMO-reMIX-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/SAINEMO-reMIX-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/SAINEMO-reMIX-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/SAINEMO-reMIX-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/SAINEMO-reMIX-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/SAINEMO-reMIX-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/SAINEMO-reMIX-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/SAINEMO-reMIX-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/SAINEMO-reMIX-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/SAINEMO-reMIX-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/SAINEMO-reMIX-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/SAINEMO-reMIX-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/SAINEMO-reMIX-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/SAINEMO-reMIX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/SAINEMO-reMIX-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SAINEMO-reMIX-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/SAINEMO-reMIX-GGUF

This is quantized version of Moraliane/SAINEMO-reMIX created using llama.cpp

Original Model Card

SAINEMO-reMIX

GGUF: thx team mradermacher

https://huggingface.co/mradermacher/SAINEMO-reMIX-GGUF

GGUF imatrix

https://huggingface.co/mradermacher/SAINEMO-reMIX-i1-GGUF

learderboard

Presets

The given presets are quite suitable for this model. https://huggingface.co/MarinaraSpaghetti/SillyTavern-Settings/tree/main/Customized/Mistral%20Improved

Sampler

Temp - 0,7 - 1,2 ~
TopA - 0,1
DRY - 0,8 1,75 2 0
I recommend trying the stock presets from SillyTavern, such as simple-1.

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

Merge Details

Merge Method

This model was merged using the della_linear merge method using E:\Programs\TextGen\text-generation-webui\models\IlyaGusev_saiga_nemo_12b as a base.

Models Merged

The following models were included in the merge:

E:\Programs\TextGen\text-generation-webui\models\elinas_Chronos-Gold-12B-1.0
E:\Programs\TextGen\text-generation-webui\models\Vikhrmodels_Vikhr-Nemo-12B-Instruct-R-21-09-24
E:\Programs\TextGen\text-generation-webui\models\MarinaraSpaghetti_NemoMix-Unleashed-12B

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: E:\Programs\TextGen\text-generation-webui\models\IlyaGusev_saiga_nemo_12b
    parameters:
      weight: 0.55  # Основной акцент на русском языке
      density: 0.4
  - model: E:\Programs\TextGen\text-generation-webui\models\MarinaraSpaghetti_NemoMix-Unleashed-12B
    parameters:
      weight: 0.2  # РП модель, чуть меньший вес из-за ориентации на английский
      density: 0.4
  - model: E:\Programs\TextGen\text-generation-webui\models\elinas_Chronos-Gold-12B-1.0
    parameters:
      weight: 0.15  # Вторая РП модель
      density: 0.4
  - model: E:\Programs\TextGen\text-generation-webui\models\Vikhrmodels_Vikhr-Nemo-12B-Instruct-R-21-09-24
    parameters:
      weight: 0.25  # Русскоязычная поддержка и баланс
      density: 0.4

merge_method: della_linear
base_model: E:\Programs\TextGen\text-generation-webui\models\IlyaGusev_saiga_nemo_12b
parameters:
  epsilon: 0.05
  lambda: 1
dtype: float16
tokenizer_source: base