Instructions to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="electroglyph/gemma-3-4b-it-unslop-GRPO-v2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("electroglyph/gemma-3-4b-it-unslop-GRPO-v2")
model = AutoModelForMultimodalLM.from_pretrained("electroglyph/gemma-3-4b-it-unslop-GRPO-v2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 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 electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

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 electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

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 electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

Use Docker

docker model run hf.co/electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electroglyph/gemma-3-4b-it-unslop-GRPO-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electroglyph/gemma-3-4b-it-unslop-GRPO-v2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

SGLang

How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 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 "electroglyph/gemma-3-4b-it-unslop-GRPO-v2" \
    --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": "electroglyph/gemma-3-4b-it-unslop-GRPO-v2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "electroglyph/gemma-3-4b-it-unslop-GRPO-v2" \
        --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": "electroglyph/gemma-3-4b-it-unslop-GRPO-v2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 with Ollama:
```
ollama run hf.co/electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL
```

Unsloth Studio

How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 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 electroglyph/gemma-3-4b-it-unslop-GRPO-v2 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 electroglyph/gemma-3-4b-it-unslop-GRPO-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for electroglyph/gemma-3-4b-it-unslop-GRPO-v2 to start chatting

Atomic Chat new
Docker Model Runner
How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 with Docker Model Runner:
```
docker model run hf.co/electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL
```

Lemonade

How to use electroglyph/gemma-3-4b-it-unslop-GRPO-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull electroglyph/gemma-3-4b-it-unslop-GRPO-v2:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma-3-4b-it-unslop-GRPO-v2-UD-Q4_K_XL

List all available models

lemonade list

Gemma 3 4b unslop experiment v2

An unslop finetune of google/gemma-3-4b-it

Updates / Observations

An updated version of this model is here: v3

I've received some excellent feedback.

Some usage notes: Low temp recommended. My training technique uses high temp to try to hit slop edge cases, but I ended up baking in some trippiness on accident I think.

Overall I'm starting to like this model. I'm going to adjust things a little bit for my next attempt and bring it back down to earth a bit, but overall it's still creative and less AI-like in a lot of ways.

Changes from my first test

I created a lot more varied example text from which I grabbed overused bigrams and trigrams. It's now 60MB of junk...I'm starting to dream about em dashes in the rain.
I completely re-did my datasets with lots of different prompt styles
Slop examples now number around 6000 in my training script. Lots of bigrams are duplicated in the trigrams, that's mostly a feature and not a bug
My 4 comma regex rule is activated about 80% thru training. First time around it was active the whole time and made the model output much shorter sentences. I'm trying to achieve a better balance this time
Trained on about double the amount of tokens
Model is still a bit underfit. I feel like I'm approaching the brain damage zone so I'm being cautious
I've uploaded a UD-Q4_K_XL GGUF with settings that I grabbed from Unsloth's quant using my lil utility: quant_clone

Training technique:

I have a pretty complex reward system, so parts are activated in 3 separate stages.

I generated lots of sample text and then sorted all bigrams and trigrams by frequency.

I added some of these to the reward function and penalized their use.

I also added some regex filters for various things.

If the prompt doesn't include "rain", but model output includes rain, it gets penalized.

Same thing for "air". Gemma 3 LOVES to talk about rain and how the air tastes (or clings, etc.)... no more.

Many of my training prompts include a word count for the model to output. Low deviation is rewarded, the opposite is penalized.

Halfway through training I activated lexical diversity comparison. It penalizes MTLD < 100, gives increasing rewards up to 120.

About 80% through training I enabled the 4+ comma per sentence regex which penalizes complex phrases.

There's a callback for early stopping if reward stays high, but it didn't kick in this run.

This was trained on ~30 million tokens on a single 3090. I'm sharing my code so people can try their own finetuning runs.

training code: train.py

Note: some of the bigrams/trigrams look funny because I've stripped any non-alpha chars from them. If you wanna use em you'll have to process your text the same way I did.

Downloads last month: 16

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for electroglyph/gemma-3-4b-it-unslop-GRPO-v2

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

(478)

this model