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.
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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):
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.
