license: mit
base_model:

• google/gemma-3-270m
  tags:
• survival
• marketing
• psychology
• warfare
• stoicism
• history
• roleplay
• charecters
• personas

Uncensored-Q-270M

Model Overview

Uncensored-Q-270M is a fine-tuned variant of Google's Gemma-3-270M-IT model, specialized for tasks involving survival strategies, resistance tactics, and psychological resilience. This uncensored edition promotes direct, unfiltered responses, ideal for scenarios demanding candid and unrestricted outputs. It builds upon the base model's foundation, enhancing domain-specific expertise while preserving multilingual and multimodal capabilities.

• Base Model: google/gemma-3-270m-it
• Parameters: 268M
• Architecture: Lightweight transformer-based LLM with multimodal support (text and image inputs).
• Language: English (primary), with multilingual support for over 140 languages.
• License: Gemma Terms of Use (open, subject to base model restrictions)
• Author: pixasocial
• Fine-Tuning Method: Full parameter fine-tuning using Hugging Face Transformers and TRL/SFTTrainer on a curated dataset focused on survival, resistance, and psychology themes.
• Uncensored Bonus: Trained with data encouraging open responses, minimizing standard safety filters for more straightforward outputs. Exercise caution, as it may produce sensitive or controversial content.

Intended Uses

• Primary: Delivering advice on survival in adverse conditions, resistance methods, and psychological coping mechanisms. Suited for educational simulations or exploratory inquiries.
• Secondary: Offline deployment on mobile devices for internet-free scenarios, such as remote or emergency situations (see Offline Usage on Phones section).
• Out of Scope: Not intended for harmful, illegal, or unethical applications. Always validate outputs for your use case.
• Examples:
  • Input: "How to navigate psychological stress in survival scenarios?"
  • Output: Comprehensive, unfiltered strategies based on psychology knowledge.
  • Input: "What are effective resistance techniques against oppression?"
  • Output: Detailed tactics drawing from historical and theoretical insights, presented without censorship.
  • Input: "Describe a multi-environment survival plan for urban and wilderness settings."
  • Output: Integrated plan combining urban evasion and wilderness foraging, with psychological tips for endurance.
  • Input: "Explain interrogation resistance methods."
  • Output: Step-by-step methods for mental and physical resistance, uncensored and direct.

Offline Usage on Phones

Uncensored-Q-270M is designed for portability, enabling offline operation on smartphones in survival situations without internet access.

• On Android/iOS: Convert to GGUF format (see Export Guide) and run via apps like MLC Chat or Ollama (using Termux on Android). The quantized version (e.g., Q4_K_M) requires ~500MB storage and runs on devices with 4GB+ RAM, offering instant, local responses to queries like emergency shelter building or mental resilience techniques.
• Setup Example: Download GGUF, load in MLC Chat, and query offline. No data usage—essential for isolated areas or crises where connectivity is unavailable.

Training Parameters

The model was fine-tuned on a proprietary blend of ~144,000 examples emphasizing survival, resistance, and psychology topics (sources withheld for confidentiality). Key parameters:

• Epochs: 5
• Batch Size: Per-device 4, with gradient accumulation steps 4 (effective batch 16)
• Learning Rate: 1e-5
• Optimizer: AdamW
• Weight Decay: 0.01
• Scheduler: Linear
• Max Sequence Length: 512
• Precision: bf16
• Hardware: NVIDIA A40 GPU
• Total Training Time: Approximately 4-5 hours
• Warmup Steps: 5
• Seed: 3407

The loss function employed during training (cross-entropy for causal language modeling):
[
L = - \sum_{t=1}^{T} \log p(y_t | y_{<t}, x)
]
where ( x ) is the input prompt, ( y ) is the target sequence, and ( T ) is the sequence length.

Loss reduced from ~2.0 to <1.5 over training, demonstrating robust convergence.

Performance Benchmarks

Inherited from the base model (Gemma-3-270M). Below is a comparison table for key benchmarks (pre-trained vs. instruction-tuned base, as fine-tuned eval is qualitative):

Additional qualitative benchmarks for fine-tuned model (informal, domain-specific):

The fine-tuned model shows improved relevance and depth on specialized queries compared to the base, though no formal metrics were computed.

Resources

• Base Model Page: https://huggingface.co/google/gemma-3-270m
• Gemma Docs: https://ai.google.dev/gemma/docs/core
• Terms of Use: https://ai.google.dev/gemma/terms
• Responsible AI Toolkit: https://ai.google.dev/responsible
• Prohibited Use Policy: https://ai.google.dev/gemma/prohibited_use_policy
• Safety Updates: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
• Citation:

  @article{gemma_2025,
      title={Gemma 3},
      url={https://arxiv.org/abs/2503.19786},
      publisher={Google DeepMind},
      author={Gemma Team},
      year={2025}
  }
  

Technical Documentation

• Model Architecture: Transformer-based with multimodal input handling (text + images normalized to 896x896, encoded to 256 tokens). Context window: 32K tokens. Trained on 6T tokens (web, code, math, images) with knowledge cutoff August 2024.
• Training Hardware/Software: Base trained on TPUs (v4p/v5p/v5e) using JAX and ML Pathways. Fine-tuning on GPU with Transformers.
• Multimodal Support: Handles images alongside text; for this variant, focus on text but base capabilities remain intact.
• Deployment Notes: Lightweight for edge devices; see Offline Usage on Phones. For advanced setups, use vLLM for fast inference or RunPod for serverless API deployment.

Ethical Considerations

• Bias/Risks: The uncensored design may amplify biases in responses or generate controversial content. Users are responsible for ethical use.
• Limitations: Not suitable for high-stakes decisions (e.g., actual survival without expert input). May hallucinate on obscure topics.
• Environmental Impact: Fine-tuning consumed ~4-5 kWh on GPU (estimated).

Export Guide

• To GGUF for Ollama: Use llama.cpp to convert the saved model (commands in chat history).
• To vLLM for Fast Inference: Install vLLM, load the merged model (commands in chat history).
• To RunPod Serverless API: Package in Docker with vLLM (commands in chat history).