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survival-uncensored-gemma-270m-v2

Text Generation
PEFT
TensorBoard
Safetensors
GGUF
Transformers
gemma3_text
lora
survival
marketing
psychology
warfare
stoicism
history
roleplay
personas
conversation
micromodels
conversational
text-generation-inference
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metadata
base_model: google/gemma-3-270m-it
library_name: peft
pipeline_tag: text-generation
tags:
  - base_model:adapter:google/gemma-3-270m-it
  - lora
  - transformers
  - survival
  - marketing
  - psychology
  - warfare
  - stoicism
  - history
  - roleplay
  - personas
  - conversation
  - micromodels
license: mit

Uncensored-Q-270M-v2

image/png

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.

image/png

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):

image/png

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. Install Ollama from ollama.com.
  2. Pull a variant: ollama pull q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf.
  3. Run: ollama run q1776/survival-uncensored-gemma-270m-v2:Q4_K_M.gguf.
  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. 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.
  2. Termux (Android): Install Termux, then Ollama. Pull and run as above.
  3. iOS: Use Ollama-compatible apps or simulators; native options limited.

Deployment on Desktop

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

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.