d4801bb4f1
Model: Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF Source: Original Platform
149 lines
4.2 KiB
Markdown
149 lines
4.2 KiB
Markdown
---
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license: gemma
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language:
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- en
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base_model:
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- google/gemma-3-1b-it
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tags:
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- uncensored
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- text-generation
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- reasoning
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- instruction-tuned
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- lightweight
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---
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# Gemma 3 – 1B IT GLM-4.7 Flash Heretic Uncensored Thinking
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This repository hosts Gemma 3 – 1B IT GLM-4.7 Flash Heretic Uncensored Thinking, a lightweight 1 billion–parameter instruction-tuned model derived from Google’s Gemma 3 1B IT base.
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This variant is optimized for fast inference, structured reasoning behavior, and minimal refusal patterns, while maintaining compatibility with Gemma’s native instruction format.
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---
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## Model Overview
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- **Model Name**: Gemma 3 – 1B IT GLM-4.7 Flash Heretic Uncensored Thinking
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- **Parameter Count**: 1 Billion (1B)
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- **Base Architecture**: Gemma 3
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- **Base Model**: google/gemma-3-1b-it
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- **Model Type**: Instruction-Tuned Causal Language Model
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- **Context Length**: Inherits base model context window
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- **Primary Language**: English
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- **License**: Gemma License (inherits from base model)
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- **Maintainer / Publisher**: DavidAU
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---
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## What Is This Model?
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This model is a modified derivative of Gemma 3 – 1B IT, configured for:
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- Reduced refusal bias compared to default IT alignment
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- Enhanced direct-answer behavior
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- Stronger short-form reasoning output
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- Faster response latency due to compact parameter size
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- “Flash”-style concise and rapid generation
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The “Heretic Uncensored Thinking” configuration emphasizes:
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- Minimal conversational filtering
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- Direct completion behavior
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- Structured reasoning patterns when prompted
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No additional safety layers beyond those present in the base architecture are intentionally introduced.
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---
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## Key Features & Capabilities
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### Core Strengths
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- Fast inference on consumer GPUs and CPUs
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- Low VRAM requirements
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- Instruction-following compatibility
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- Concise reasoning outputs
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- Suitable for lightweight agent pipelines
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### Performance Characteristics
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- Optimized for short-to-medium generation tasks
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- Responsive in real-time assistant applications
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- Works well in tool-driven or chain-of-thought–style prompts
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- Practical for edge deployments and experimentation
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---
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## Intended Use Cases
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- Lightweight AI assistant
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- Prompt engineering experimentation
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- Tool-augmented agents
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- Rapid-response chat systems
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- Local inference environments
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- Educational or research workflows
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- Controlled “uncensored” deployment environments
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---
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## Chat Template & Prompt Format
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This model follows the Gemma instruction format.
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For best results:
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- Provide explicit system instructions
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- Use structured reasoning prompts when needed
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- Avoid mixing non-Gemma chat formats
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---
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## Hardware & Deployment Notes
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Due to its 1B parameter size:
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- Runs efficiently on 8GB GPUs
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- Suitable for CPU inference with quantization
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- Ideal for edge devices and low-resource setups
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- Compatible with common inference engines supporting Gemma architecture
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Quantized versions (GGUF, GPTQ, AWQ, etc.) may be used depending on deployment stack.
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---
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## Alignment & Safety Notice
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This is an “uncensored” derivative configuration.
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- Reduced refusal behavior compared to standard IT
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- Users are responsible for system prompt controls
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- Deployment should follow local laws and ethical guidelines
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- No additional alignment layers are added by this repository
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Use responsibly.
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---
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## License & Usage Notes
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This model inherits the **Gemma License** from its base model (*google/gemma-3-1b-it*).
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- The Gemma License is a custom license provided by Google
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- You must review and comply with the Gemma License terms
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- This repository does not change or replace the original licensing terms
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Users are responsible for ensuring compliance with all applicable regulations.
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---
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## Acknowledgements
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- Google for the Gemma 3 architecture and base model
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- The Hugging Face ecosystem
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- Open-source tooling communities supporting lightweight deployment
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---
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## Community & Support
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- Use the Hugging Face Discussions tab for issues and questions
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- Community experimentation and benchmarking feedback is welcome
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