Instructions to use QuantFactory/gemma-2-Ifable-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/gemma-2-Ifable-9B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/gemma-2-Ifable-9B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/gemma-2-Ifable-9B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/gemma-2-Ifable-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/gemma-2-Ifable-9B-GGUF",
	filename="gemma-2-Ifable-9B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/gemma-2-Ifable-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/gemma-2-Ifable-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/gemma-2-Ifable-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/gemma-2-Ifable-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/gemma-2-Ifable-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/gemma-2-Ifable-9B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/gemma-2-Ifable-9B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/gemma-2-Ifable-9B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/gemma-2-Ifable-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/gemma-2-Ifable-9B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/gemma-2-Ifable-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/gemma-2-Ifable-9B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/gemma-2-Ifable-9B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-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/gemma-2-Ifable-9B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/gemma-2-Ifable-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/gemma-2-Ifable-9B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/gemma-2-Ifable-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/gemma-2-Ifable-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2-Ifable-9B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/gemma-2-Ifable-9B-GGUF

This is quantized version of ifable/gemma-2-Ifable-9B created using llama.cpp

Original Model Card

ifable/gemma-2-Ifable-9B

Training and evaluation data

Gutenberg: https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1
Carefully curated proprietary creative writing dataset

Training procedure

Training method: SimPO (GitHub - princeton-nlp/SimPO: SimPO: Simple Preference Optimization with a Reference-Free Reward)

It achieves the following results on the evaluation set:

Loss: 1.0163
Rewards/chosen: -21.6822
Rewards/rejected: -47.8754
Rewards/accuracies: 0.9167
Rewards/margins: 26.1931
Logps/rejected: -4.7875
Logps/chosen: -2.1682
Logits/rejected: -17.0475
Logits/chosen: -12.0041

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 8e-07
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	Sft Loss
1.4444	0.9807	35	1.0163	-21.6822	-47.8754	0.9167	26.1931	-4.7875	-2.1682	-17.0475	-12.0041	0.0184

Framework versions

Transformers 4.43.4
Pytorch 2.3.0a0+ebedce2
Datasets 2.20.0
Tokenizers 0.19.1

We are looking for product manager and operations managers to build applications through our model, and also open for business cooperation, and also AI engineer to join us, contact with : contact@ifable.ai

Downloads last month: 270

GGUF

Model size

9B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit