pixasocial/survival-uncensored-gemma-270m-v2

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 Q4KM 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:

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.

1. 1.Install Ollama from ollama.com.
2. 2.Pull a variant: ollama pull q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf.
3. 3.Run: ollama run q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf.
4. 4.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:

1. 1.MLC Chat: Download from mlc.ai. Import GGUF (e.g., Q4KM, 253 MB) and query offline. Requires 4GB RAM; expect 5-10 tokens/second.
2. 2.Termux (Android): Install Termux, then Ollama. Pull and run as above.
3. 3.iOS: Use Ollama-compatible apps or simulators; native options limited.

Deployment on Desktop

1. 1.LM Studio: From lmstudio.ai; import GGUF and use UI.
2. 2.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):

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.