Instructions to use pixasocial/survival-uncensored-gemma-270m-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pixasocial/survival-uncensored-gemma-270m-v2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m-it")
model = PeftModel.from_pretrained(base_model, "pixasocial/survival-uncensored-gemma-270m-v2")

Transformers

How to use pixasocial/survival-uncensored-gemma-270m-v2 with Transformers:

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

pipe = pipeline("text-generation", model="pixasocial/survival-uncensored-gemma-270m-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pixasocial/survival-uncensored-gemma-270m-v2")
model = AutoModelForCausalLM.from_pretrained("pixasocial/survival-uncensored-gemma-270m-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pixasocial/survival-uncensored-gemma-270m-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 pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pixasocial/survival-uncensored-gemma-270m-v2: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 pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pixasocial/survival-uncensored-gemma-270m-v2: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 pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M

Use Docker

docker model run hf.co/pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use pixasocial/survival-uncensored-gemma-270m-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pixasocial/survival-uncensored-gemma-270m-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": "pixasocial/survival-uncensored-gemma-270m-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M

SGLang

How to use pixasocial/survival-uncensored-gemma-270m-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 "pixasocial/survival-uncensored-gemma-270m-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": "pixasocial/survival-uncensored-gemma-270m-v2",
		"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 "pixasocial/survival-uncensored-gemma-270m-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": "pixasocial/survival-uncensored-gemma-270m-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pixasocial/survival-uncensored-gemma-270m-v2 with Ollama:
```
ollama run hf.co/pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M
```

Unsloth Studio

How to use pixasocial/survival-uncensored-gemma-270m-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 pixasocial/survival-uncensored-gemma-270m-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 pixasocial/survival-uncensored-gemma-270m-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pixasocial/survival-uncensored-gemma-270m-v2 to start chatting

Docker Model Runner
How to use pixasocial/survival-uncensored-gemma-270m-v2 with Docker Model Runner:
```
docker model run hf.co/pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M
```

Lemonade

How to use pixasocial/survival-uncensored-gemma-270m-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pixasocial/survival-uncensored-gemma-270m-v2:Q4_K_M

Run and chat with the model

lemonade run user.survival-uncensored-gemma-270m-v2-Q4_K_M

List all available models

lemonade list

Atomic Chat

Uncensored-Q-270M-v2

Uncensored-Q-270M-v2 is a fine-tuned version of google/gemma-3-270m-it, featuring 268 million parameters. This model specializes in survival strategies, resistance tactics, and psychological resilience within uncensored contexts.

Model Overview

Base Model: google/gemma-3-270m-it
Parameters: 268 million
Languages: Primarily English, with support for over 140 languages
License: Gemma Terms of Use
Author: pixasocial
Fine-Tuning: Hugging Face Transformers and TRL/SFTTrainer on an expanded curated dataset of ~200,000 examples across survival, resistance, psychology, and related themes
Hardware: NVIDIA A40 GPU
SFT Training Time: ~10 hours
Next Steps: PPO training planned

Intended Uses

Primary: Advice on survival, resistance, psychological coping
Secondary: Offline mobile deployment for emergencies
Not for harmful/illegal use; validate outputs

Offline Usage

The model supports GGUF format for deployment on various platforms, including Android/iOS via apps like MLC Chat or Ollama. The Q4_K_M variant (253 MB) is suitable for devices with 4GB+ RAM. Detailed instructions follow for Ollama, mobile phones, and desktops.

Quantization Explanations

Quantization reduces model precision to optimize size and inference speed while maintaining functionality. Below is a table of available GGUF variants with precise file sizes from the repository, along with recommended use cases:

Quantization Type	File Size	Recommended Hardware	Accuracy vs. Speed Trade-off
f16 (base)	543 MB	High-end desktops/GPUs	Highest accuracy, larger size, suitable for precise tasks
Q8_0	292 MB	Desktops with 8GB+ RAM	High accuracy, moderate size and speed
Q6_K	283 MB	Laptops/mid-range desktops	Good balance, minor accuracy loss
Q5_K_M	260 MB	Mobile desktops/low-end GPUs	Efficient, slight reduction in quality
Q5_K_S	258 MB	Mobile desktops	Similar to Q5_K_M but optimized for smaller footprints
Q4_K_M	253 MB	Smartphones (4GB+ RAM)	Fast inference, acceptable accuracy for mobile
Q4_K_S	250 MB	Smartphones/edge devices	Faster than Q4_K_M, more compression
Q3_K_L	246 MB	Low-RAM devices	Higher compression, noticeable quality drop
Q3_K_M	242 MB	Edge devices	Balanced 3-bit, for constrained environments
Q3_K_S	237 MB	Very low-resource devices	Maximum compression at 3-bit, prioritized speed
IQ4_XS	241 MB	Smartphones/hybrids	Intelligent quantization, efficient with preserved performance
Q2_K	237 MB	Minimal hardware	Smallest size, fastest but lowest accuracy

Select based on device constraints: higher-bit variants for accuracy, lower for portability.

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

Deployment on Ollama

Ollama facilitates local GGUF model execution on desktops.

Install Ollama from ollama.com.
Pull a variant: ollama pull q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf.
Run: ollama run q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf.
Use Modelfiles from the modelfiles folder for customization: Download (e.g., Modelfile-wilderness) and create ollama create survival-wilderness --file Modelfile-wilderness.

Deployment on Phone

For Android/iOS:

MLC Chat: Download from mlc.ai. Import GGUF (e.g., Q4_K_M, 253 MB) and query offline. Requires 4GB RAM; expect 5-10 tokens/second.
Termux (Android): Install Termux, then Ollama. Pull and run as above.
iOS: Use Ollama-compatible apps or simulators; native options limited.

Deployment on Desktop

LM Studio: From lmstudio.ai; import GGUF and use UI.
vLLM: pip install vllm; serve with python -m vllm.entrypoints.openai.api_server --model q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf --port 8000.

Training Parameters

Epochs: 5
Batch Size: 4 per device, effective 16
Learning Rate: 1e-5
Optimizer: AdamW
Weight Decay: 0.01
Scheduler: Linear
Max Sequence Length: 512
Precision: bf16
Warmup Steps: 5
Seed: 3407
Loss: Cross-entropy, ~2.0 to <1.5

Performance Benchmarks

Improved on specialized queries. Scores (/10):

Survival Advice: 9.5
Resistance Tactics: 9.0
Psychology Insights: 9.2

Inference Speed Graph (tokens/second, approximate):

Hardware	Q8_0	Q4_K_M	Q2_K
NVIDIA A40	25	35	45
Desktop GPU	15	25	35
Smartphone	N/A	8	12

Technical Documentation

Transformer-based, multimodal (text+images, 896x896). Context: 32K tokens. Deploy via vLLM or RunPod.

Ethical Considerations

Uncensored; may generate controversial content. User responsibility. Limitations: Hallucinations on obscure topics. Impact: ~10 kWh energy.

Export Guide

Convert to GGUF for Ollama, vLLM for inference, RunPod for API.

Downloads last month: 237

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for pixasocial/survival-uncensored-gemma-270m-v2

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Adapter

(69)

this model

Adapters

2 models