Instructions to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF", dtype="auto") - llama-cpp-python
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF", filename="gemma-2-9B-it-advanced-v2.1-F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-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 jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16 # Run inference directly in the terminal: llama cli -hf jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16 # Run inference directly in the terminal: llama cli -hf jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
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 jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
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 jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
Use Docker
docker model run hf.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF with Ollama:
ollama run hf.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
- Unsloth Studio
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-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 jsgreenawalt/gemma-2-9B-it-advanced-v2.1-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 jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF with Docker Model Runner:
docker model run hf.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
- Lemonade
How to use jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF:F16
Run and chat with the model
lemonade run user.gemma-2-9B-it-advanced-v2.1-GGUF-F16
List all available models
lemonade list
Gemma Advanced V2.1 - GGUF Quants
This is a merge of the 'smartest' advanced fine-tunes available for Gemma-2-9b-it. It includes WPO, SimPO, and SPPO. The merge was performed via the SOTA 'della' merge method. Merge parameters have been hand-tuned for best results. The Q8_0 quant is highly recommended. Other quants are linked below.
Notes and observations:
- The extreme temperature sensitivity from V1 has been fixed, no longer needs to be run at lower temperatures
- Has a somewhat different writing style than any of the parent models
- Great instruction following
- Tracks plot details well and has good situational understanding
- Seems to have a good understanding of psychology, emotions and creative writing
- More 'sane' than base gemma-it, SPPO, or SimPO - not as prone to 'Cruella De Vil' or 'Evil Sorceress' like SPPO or SimPO, when portraying characters
- Would likely serve as a good base for further merges
- I'm looking for a job, if you're hiring. I'm a skilled Python developer who brings strong devops skills along with an ever-growing knowledge of machine learning pipelines and models. Message me if you want to talk about what I can bring to your team.
- Overall, this feels like a very useful and successful merge.
Other Quantized GGUFs can be found here:
- iMatrix - mradermacher/gemma-2-9B-it-advanced-v2.1-i1-GGUF
- QuantFactory/gemma-2-9B-it-advanced-v2.1-GGUF
- mradermacher/gemma-2-9B-it-advanced-v2.1-GGUF
Thanks to everyone who was kind enough to provide quants!
sample ollama Modelfile
FROM /path/to/file/gemma-2-9B-it-advanced-v2.1-Q8_0.gguf
PARAMETER stop "<start_of_turn>"
PARAMETER stop "<end_of_turn>"
PARAMETER num_ctx 8192
TEMPLATE """<start_of_turn>user
{{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}<end_of_turn>
<start_of_turn>model
{{ .Response }}<end_of_turn>"""
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the della merge method using google/gemma-2-9b-it 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: google/gemma-2-9b-it
- model: wzhouad/gemma-2-9b-it-WPO-HB
parameters:
density: 0.55
weight: 0.6
- model: princeton-nlp/gemma-2-9b-it-SimPO
parameters:
density: 0.35
weight: 0.6
- model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
parameters:
density: 0.25
weight: 0.4
merge_method: della
base_model: google/gemma-2-9b-it
parameters:
normalize: true
int8_mask: true
lambda: 1.0
epsilon: 0.1
dtype: float16
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