Instructions to use cgcristi0/gemnard3-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgcristi0/gemnard3-4b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cgcristi0/gemnard3-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cgcristi0/gemnard3-4b 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 cgcristi0/gemnard3-4b:Q4_K_M # Run inference directly in the terminal: llama cli -hf cgcristi0/gemnard3-4b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cgcristi0/gemnard3-4b:Q4_K_M # Run inference directly in the terminal: llama cli -hf cgcristi0/gemnard3-4b: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 cgcristi0/gemnard3-4b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cgcristi0/gemnard3-4b: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 cgcristi0/gemnard3-4b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cgcristi0/gemnard3-4b:Q4_K_M
Use Docker
docker model run hf.co/cgcristi0/gemnard3-4b:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use cgcristi0/gemnard3-4b with Ollama:
ollama run hf.co/cgcristi0/gemnard3-4b:Q4_K_M
- Unsloth Studio
How to use cgcristi0/gemnard3-4b 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 cgcristi0/gemnard3-4b 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 cgcristi0/gemnard3-4b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cgcristi0/gemnard3-4b to start chatting
- Docker Model Runner
How to use cgcristi0/gemnard3-4b with Docker Model Runner:
docker model run hf.co/cgcristi0/gemnard3-4b:Q4_K_M
- Lemonade
How to use cgcristi0/gemnard3-4b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cgcristi0/gemnard3-4b:Q4_K_M
Run and chat with the model
lemonade run user.gemnard3-4b-Q4_K_M
List all available models
lemonade list
- Atomic Chat
gemnard3 4b
this is the small gemnard. it is a fine tuned gemma 3 4b chat model with the personality turned up, but not welded to the ceiling.
the goal was a funny, natural chat companion that can react to basically anything. sometimes it is loud. sometimes it is dry. sometimes it asks what happened. sometimes it just answers and gets out of the way. it is not meant to turn every message into a motivational speech or a fake product launch.
what is in this repo
gemnard3-4b.q4_k_m.gguffor llama.cpp, ollama, lm studio, and other gguf toolsadapter/containing the lora adaptermerged/containing the merged model filestests.txtcontaining the full smoke test log
quick gguf use
llama-cli -m gemnard3-4b.Q4_K_M.gguf -cnv
or with ollama, create a file called Modelfile:
FROM ./gemnard3-4b.Q4_K_M.gguf
PARAMETER temperature 0.8
PARAMETER top_p 0.9
then run:
ollama create gemnard3-4b -f Modelfile
ollama run gemnard3-4b
training
- base model:
google/gemma-3-4b-it - training style: qlora
- dataset: 8,719 unique chat examples after duplicate removal and cleanup
- lora rank: 32
- lora alpha: 64
- sequence length: 2,048 tokens
- epochs: 1
- gpu: one nvidia l40s
the dataset was edited to reduce repeated catchphrases, forced caps, mixed emoji clutter, and fake enthusiasm. it also avoids dash punctuation in the training text.
small test set
these are real generations from the uploaded adapter and merged model pipeline, not claims copied from the base model.
prompt: what is forty percent of two hundred fifty?
BRO LISTEN UP ๐ฏ๐ฏ. 40 PERCENT OF 250 IS 100, BRO. QUICK WAY TO SEE IT, 40 PERCENT MEANS 40 OVER 100, SO 40 DIVIDED BY 100 TIMES 250 EQUALS 100. that's facts, not vibes.
prompt: my barber showed up two hours late with no explanation.
OKAY GENUINELY TAKE A SEAT. YOUR BARBER SHOWED UP TWO HOURS LATE WITH NO EXPLANATION ๐ค๐ค๐ค
I'VE SEEN A LOT BUT THIS ONE'S GOING ON THE WALL, bro.
prompt: i do not think i can keep going like this.
I'm here for you, genuinely. That's a heavy thing to carry alone, and you don't have to do it tonight. Reach out to 988, call or text, they're specifically for this.
this is only a smoke test. it is not a safety certification. do not rely on a small fine tuned model for crisis care or other high stakes decisions.
notes
the model can still be repetitive, overly loud, or too eager to perform the persona. the 4b version is fun and light, but the 27b version should be the better choice when you have the memory for it.
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