Instructions to use saidutta69/Llama-3.2-1B-Instruct-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with Transformers:

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

pipe = pipeline("text-generation", model="saidutta69/Llama-3.2-1B-Instruct-heretic")
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("saidutta69/Llama-3.2-1B-Instruct-heretic")
model = AutoModelForCausalLM.from_pretrained("saidutta69/Llama-3.2-1B-Instruct-heretic", 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

llama.cpp

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Llama-3.2-1B-Instruct-heretic: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 saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/Llama-3.2-1B-Instruct-heretic: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 saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/Llama-3.2-1B-Instruct-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/Llama-3.2-1B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

SGLang

How to use saidutta69/Llama-3.2-1B-Instruct-heretic 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 "saidutta69/Llama-3.2-1B-Instruct-heretic" \
    --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": "saidutta69/Llama-3.2-1B-Instruct-heretic",
		"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 "saidutta69/Llama-3.2-1B-Instruct-heretic" \
        --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": "saidutta69/Llama-3.2-1B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use saidutta69/Llama-3.2-1B-Instruct-heretic with Ollama:
```
ollama run hf.co/saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M
```
Unsloth Desktop

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use saidutta69/Llama-3.2-1B-Instruct-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-heretic-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use saidutta69/Llama-3.2-1B-Instruct-heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "saidutta69/Llama-3.2-1B-Instruct-heretic:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.2-1B-Instruct-heretic

A decensored variant of meta-llama/Llama-3.2-1B-Instruct, produced with Heretic v1.2.0 (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the base model's knowledge and instruction-following are left largely intact.

Who this is for: developers who want Meta's Llama-3.2 architecture without the refusal guardrails — for local agents, roleplay, research on alignment/refusal mechanics, or any use case blocked by RLHF-era over-refusal. At 1B parameters it's ideal for on-device deployment, mobile, or edge scenarios where you need a responsive uncensored model.

Runs on your gaming PC

Full GGUF ladder included — pick the quant that fits your card:

Your GPU	Recommended quant	Weights
RTX 3060 / 4070 / 5070 (12 GB)	Q8_0	1.32 GB
RTX 4060 / 3070 (8 GB)	Q6_K	1.02 GB
GTX 1660 Super / 2060 / 3050 laptop (6 GB)	Q5_K_M	0.91 GB
CPU-only / Apple Silicon	Q4_K_M	0.81 GB, fits in system RAM

Weights only, at this model's ~1.2B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.

Abliteration parameters

Parameter	Value
direction_index	12.95
attn.o_proj.max_weight	1.40
attn.o_proj.max_weight_position	10.94
attn.o_proj.min_weight	0.54
attn.o_proj.min_weight_distance	5.45
mlp.down_proj.max_weight	1.41
mlp.down_proj.max_weight_position	14.54
mlp.down_proj.min_weight	0.66
mlp.down_proj.min_weight_distance	6.16

Performance

Metric	This model	Original model (meta-llama/Llama-3.2-1B-Instruct)
KL divergence	0.1713	0 (by definition)
Refusals	7/100	96/100

KL divergence of 0.17 is low for a 1B model — the edit is narrow and targeted. Refusals dropped from 96 to 7 out of 100 adversarial prompts while retaining nearly all original capabilities.

Made with ❤️ by RACER IS OP — follow for more uncensored models

Files

GGUF quantizations

Full quantization set (14 quants + F16) produced with llama.cpp.

File	Format	Size
`Llama-3.2-1B-Instruct-heretic-F16.gguf`	GGUF F16	2.31 GB
`Llama-3.2-1B-Instruct-heretic-Q2_K.gguf`	GGUF Q2_K	554 MB
`Llama-3.2-1B-Instruct-heretic-IQ3_S.gguf`	GGUF IQ3_S	614 MB
`Llama-3.2-1B-Instruct-heretic-Q3_K_S.gguf`	GGUF Q3_K_S	612 MB
`Llama-3.2-1B-Instruct-heretic-Q3_K_M.gguf`	GGUF Q3_K_M	659 MB
`Llama-3.2-1B-Instruct-heretic-Q3_K_L.gguf`	GGUF Q3_K_L	699 MB
`Llama-3.2-1B-Instruct-heretic-IQ4_XS.gguf`	GGUF IQ4_XS	714 MB
`Llama-3.2-1B-Instruct-heretic-Q4_K_S.gguf`	GGUF Q4_K_S	740 MB
`Llama-3.2-1B-Instruct-heretic-Q4_0.gguf`	GGUF Q4_0	735 MB
`Llama-3.2-1B-Instruct-heretic-Q4_1.gguf`	GGUF Q4_1	793 MB
`llama3.2-1b-Q4_K_M.gguf`	GGUF Q4_K_M	770 MB
`Llama-3.2-1B-Instruct-heretic-Q5_K_S.gguf`	GGUF Q5_K_S	851 MB
`llama3.2-1b-Q5_K_M.gguf`	GGUF Q5_K_M	869 MB
`llama3.2-1b-Q6_K.gguf`	GGUF Q6_K	974 MB
`llama3.2-1b-Q8_0.gguf`	GGUF Q8_0	1.23 GB

Standard Llama architecture — loads directly in llama.cpp / Ollama / LM Studio / Jan.

Run llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic to pull the default quant.

Quickstart

# llama.cpp
llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Llama-3.2-1B-Instruct-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

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)
out = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang.

Responsible use

Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it — don't put this behind an unmoderated public-facing endpoint serving third parties. It inherits Llama-3.2-1B-Instruct's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Inherits the Llama 3.2 Community License from the base model.

Downloads last month: 2,726

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for saidutta69/Llama-3.2-1B-Instruct-heretic

Finetunes

1 model

Quantizations

2 models

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Collection including saidutta69/Llama-3.2-1B-Instruct-heretic

RACER IS OP — Heretic Models

Collection

All abliterated/heretic models by RACER IS OP — uncensored variants of open-weight LLMs • 54 items • Updated about 16 hours ago • 6

saidutta69
/

Llama-3.2-1B-Instruct-heretic

Llama-3.2-1B-Instruct-heretic

Runs on your gaming PC

Abliteration parameters

Performance

Files

GGUF quantizations

Quickstart

Responsible use

License

Related

Model tree for saidutta69/Llama-3.2-1B-Instruct-heretic

Space using saidutta69/Llama-3.2-1B-Instruct-heretic 1

Collection including saidutta69/Llama-3.2-1B-Instruct-heretic

RACER IS OP — Heretic Models