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

Libraries

How to use QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF",
	filename="Gemma-2-2B-Stheno-Filtered.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/Gemma-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Gemma-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Gemma-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Gemma-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Gemma-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-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-2-2B-Stheno-Filtered-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-2-2B-Stheno-Filtered-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF

This is quantized version of SaisExperiments/Gemma-2-2B-Stheno-Filtered created using llama.cpp

Original Model Card

I don't have anything else so you get a cursed cat image

Basic info

This is anthracite-org/stheno-filtered-v1.1 over unsloth/gemma-2-2b-it

It saw 76.6M tokens

This time it took 14 hours and i'm pretty sure i've been training with the wrong prompt template X-X

Training config:

cutoff_len: 1024
dataset: stheno-3.4
dataset_dir: data
ddp_timeout: 180000000
do_train: true
finetuning_type: lora
flash_attn: auto
fp16: true
gradient_accumulation_steps: 8
include_num_input_tokens_seen: true
learning_rate: 5.0e-05
logging_steps: 5
lora_alpha: 64
lora_dropout: 0
lora_rank: 64
lora_target: all
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_samples: 100000
model_name_or_path: unsloth/gemma-2-2b-it
num_train_epochs: 3.0
optim: adamw_8bit
output_dir: saves/Gemma-2-2B-Chat/lora/stheno
packing: false
per_device_train_batch_size: 2
plot_loss: true
preprocessing_num_workers: 16
quantization_bit: 4
quantization_method: bitsandbytes
report_to: none
save_steps: 100
stage: sft
template: gemma
use_unsloth: true
warmup_steps: 0

Downloads last month: 115

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Quantized

(177)

this model

QuantFactory
/

Gemma-2-2B-Stheno-Filtered-GGUF

QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF

Original Model Card

Basic info

Training config:

Model tree for QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF

Dataset used to train QuantFactory/Gemma-2-2B-Stheno-Filtered-GGUF