Instructions to use QuantFactory/Memory-9-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Memory-9-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Memory-9-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Memory-9-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/Memory-9-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Memory-9-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/Memory-9-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Memory-9-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/Memory-9-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Memory-9-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/Memory-9-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Memory-9-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Memory-9-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Memory-9-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Memory-9-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Memory-9-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/Memory-9-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/Memory-9-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/Memory-9-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Memory-9-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Memory-9-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Memory-9-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Memory-9-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Memory-9-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Memory-9-GGUF

File size: 1,481 Bytes

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---

base_model:
- ArliAI/Gemma-2-9B-ArliAI-RPMax-v1.1
- UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
library_name: transformers
tags:
- mergekit
- merge


---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/Memory-9-GGUF
This is quantized version of [ClaudioItaly/Memory-9](https://huggingface.co/ClaudioItaly/Memory-9) created using llama.cpp

# Original Model Card

# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [ArliAI/Gemma-2-9B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Gemma-2-9B-ArliAI-RPMax-v1.1)
* [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: ArliAI/Gemma-2-9B-ArliAI-RPMax-v1.1
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
merge_method: slerp
base_model: ArliAI/Gemma-2-9B-ArliAI-RPMax-v1.1
dtype: bfloat16
parameters:
  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers

```