Instructions to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF",
	filename="Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

Use Docker

docker model run hf.co/Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

Ollama
How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with Ollama:
```
ollama run hf.co/Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M
```

Unsloth Studio

How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with Docker Model Runner:
```
docker model run hf.co/Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M
```

Lemonade

How to use Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF-Q4_K_M

List all available models

lemonade list

Gemma 3 – 1B IT GLM-4.7 Flash Heretic Uncensored Thinking

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.

This variant is optimized for fast inference, structured reasoning behavior, and minimal refusal patterns, while maintaining compatibility with Gemma’s native instruction format.

Model Overview

Model Name: Gemma 3 – 1B IT GLM-4.7 Flash Heretic Uncensored Thinking
Parameter Count: 1 Billion (1B)
Base Architecture: Gemma 3
Base Model: google/gemma-3-1b-it
Model Type: Instruction-Tuned Causal Language Model
Context Length: Inherits base model context window
Primary Language: English
License: Gemma License (inherits from base model)
Maintainer / Publisher: DavidAU

What Is This Model?

This model is a modified derivative of Gemma 3 – 1B IT, configured for:

Reduced refusal bias compared to default IT alignment
Enhanced direct-answer behavior
Stronger short-form reasoning output
Faster response latency due to compact parameter size
“Flash”-style concise and rapid generation

The “Heretic Uncensored Thinking” configuration emphasizes:

Minimal conversational filtering
Direct completion behavior
Structured reasoning patterns when prompted

No additional safety layers beyond those present in the base architecture are intentionally introduced.

Key Features & Capabilities

Core Strengths

Fast inference on consumer GPUs and CPUs
Low VRAM requirements
Instruction-following compatibility
Concise reasoning outputs
Suitable for lightweight agent pipelines

Performance Characteristics

Optimized for short-to-medium generation tasks
Responsive in real-time assistant applications
Works well in tool-driven or chain-of-thought–style prompts
Practical for edge deployments and experimentation

Intended Use Cases

Lightweight AI assistant
Prompt engineering experimentation
Tool-augmented agents
Rapid-response chat systems
Local inference environments
Educational or research workflows
Controlled “uncensored” deployment environments

Chat Template & Prompt Format

This model follows the Gemma instruction format.

For best results:

Provide explicit system instructions
Use structured reasoning prompts when needed
Avoid mixing non-Gemma chat formats

Hardware & Deployment Notes

Due to its 1B parameter size:

Runs efficiently on 8GB GPUs
Suitable for CPU inference with quantization
Ideal for edge devices and low-resource setups
Compatible with common inference engines supporting Gemma architecture

Quantized versions (GGUF, GPTQ, AWQ, etc.) may be used depending on deployment stack.

Alignment & Safety Notice

This is an “uncensored” derivative configuration.

Reduced refusal behavior compared to standard IT
Users are responsible for system prompt controls
Deployment should follow local laws and ethical guidelines
No additional alignment layers are added by this repository

Use responsibly.

License & Usage Notes

This model inherits the Gemma License from its base model (google/gemma-3-1b-it).

The Gemma License is a custom license provided by Google
You must review and comply with the Gemma License terms
This repository does not change or replace the original licensing terms

Users are responsible for ensuring compliance with all applicable regulations.

Acknowledgements

Google for the Gemma 3 architecture and base model
The Hugging Face ecosystem
Open-source tooling communities supporting lightweight deployment

Community & Support

Use the Hugging Face Discussions tab for issues and questions
Community experimentation and benchmarking feedback is welcome

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GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Quantized

(441)

this model

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UNCENSORED MODELS

Collection

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