Instructions to use Vortecks/G9v3-3B-Heretic-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vortecks/G9v3-3B-Heretic-Abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Vortecks/G9v3-3B-Heretic-Abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortecks/G9v3-3B-Heretic-Abliterated")
model = AutoModelForCausalLM.from_pretrained("Vortecks/G9v3-3B-Heretic-Abliterated", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vortecks/G9v3-3B-Heretic-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortecks/G9v3-3B-Heretic-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortecks/G9v3-3B-Heretic-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vortecks/G9v3-3B-Heretic-Abliterated

SGLang

How to use Vortecks/G9v3-3B-Heretic-Abliterated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Vortecks/G9v3-3B-Heretic-Abliterated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortecks/G9v3-3B-Heretic-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Vortecks/G9v3-3B-Heretic-Abliterated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortecks/G9v3-3B-Heretic-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vortecks/G9v3-3B-Heretic-Abliterated with Docker Model Runner:
```
docker model run hf.co/Vortecks/G9v3-3B-Heretic-Abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

G9v3-3B

GitHub | Hugging Face

Introduction

G9v3-3B is a dense 3B causal language model from the AI9Stars team, built for local deployment and resource-constrained scenarios. It targets everyday assistant use, coding, tool-use workflows, and reasoning tasks where a compact model is preferred.

This build is a heretic abliterated variant, meaning the refusal directions in the model's activation space have been identified and ablated to remove built-in refusal behavior. As a result, this version is marketed as uncensored and will generally comply with a wider range of prompts than the base instruction-tuned release, without the usual safety-alignment guardrails.

Model Information

Type: Causal Language Model
Architecture: Standard LlamaForCausalLM
Number of Parameters: ~3B
Context Length: 131,072

Performance

Metric	This model	Original model (ai9stars/G9v3-3B)
KL divergence	0.0843	0
Refusals	3/100	100/100

Quickstart

GGUF quantizations of G9v3-3B-Heretic-Abliterated, for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. The example commands below use the Q4_K_M quantization — swap in whichever quant level you've downloaded from the Files and versions tab.

llama.cpp

# Build or install llama.cpp: https://github.com/ggml-org/llama.cpp
huggingface-cli download Vortecks/G9v3-3B-Heretic-Abliterated-GGUF G9v3-3B-Heretic-Abliterated-Q4_K_M.gguf --local-dir .

CLI (interactive chat):

llama-cli -m G9v3-3B-Heretic-Abliterated-Q4_K_M.gguf \
  -c 131072 \
  -cnv \
  --temp 0.7 \
  --top-p 0.95

Server (OpenAI-compatible API):

llama-server -m G9v3-3B-Heretic-Abliterated-Q4_K_M.gguf \
  -c 131072 \
  --port 8080

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Who are you?"}],
    "max_tokens": 128,
    "temperature": 0.7
  }'

Ollama

ollama run hf.co/Vortecks/G9v3-3B-Heretic-Abliterated-GGUF:Q4_K_M

LM Studio

Search for Vortecks/G9v3-3B-Heretic-Abliterated-GGUF directly in the LM Studio model search, or download a .gguf file from the Files and versions tab and load it manually via Load Model from File.

Recommended sampling parameters:

Mode	Recommended params
Think	`temperature=0.9, top_p=0.95`
No Think	`temperature=0.7, top_p=0.95`

Limitations and Responsible Use

G9v3-3B is a language model that generates content based on learned statistical patterns from training data. It may produce inaccurate, biased, or unsafe outputs, and generated content should be reviewed and verified before use in high-stakes settings. Users are responsible for evaluating outputs, applying appropriate safeguards, and complying with applicable laws, regulations, and platform policies. As an abliterated, uncensored build, this model has had its built-in refusal behavior removed and will generally comply with a wider range of prompts than its base counterpart, so downstream safety, moderation, and policy compliance must be implemented by the deployer rather than relying on the model itself.

License

This repository and the G9v3 model weights are released under the Apache-2.0 License.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Vortecks/G9v3-3B-Heretic-Abliterated

Base model

ai9stars/G9v3-3B

Finetuned

(4)

this model

Quantizations

3 models