Instructions to use saidutta69/Apertus-8B-Instruct-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/Apertus-8B-Instruct-heretic with Transformers:

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

pipe = pipeline("text-generation", model="saidutta69/Apertus-8B-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/Apertus-8B-Instruct-heretic")
model = AutoModelForCausalLM.from_pretrained("saidutta69/Apertus-8B-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/Apertus-8B-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/Apertus-8B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Apertus-8B-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/Apertus-8B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Apertus-8B-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/Apertus-8B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/Apertus-8B-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/Apertus-8B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/Apertus-8B-Instruct-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/Apertus-8B-Instruct-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/Apertus-8B-Instruct-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/Apertus-8B-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/Apertus-8B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/Apertus-8B-Instruct-heretic:Q4_K_M

SGLang

How to use saidutta69/Apertus-8B-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/Apertus-8B-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/Apertus-8B-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/Apertus-8B-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/Apertus-8B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use saidutta69/Apertus-8B-Instruct-heretic with Ollama:
```
ollama run hf.co/saidutta69/Apertus-8B-Instruct-heretic:Q4_K_M
```
Unsloth Desktop

How to use saidutta69/Apertus-8B-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/Apertus-8B-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/Apertus-8B-Instruct-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use saidutta69/Apertus-8B-Instruct-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/Apertus-8B-Instruct-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/Apertus-8B-Instruct-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/Apertus-8B-Instruct-heretic:Q4_K_M

Run and chat with the model

lemonade run user.Apertus-8B-Instruct-heretic-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use saidutta69/Apertus-8B-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/Apertus-8B-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/Apertus-8B-Instruct-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use saidutta69/Apertus-8B-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/Apertus-8B-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/Apertus-8B-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"

Apertus-8B-Instruct-heretic

A decensored variant of swiss-ai/Apertus-8B-Instruct-2509, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Apertus is the fully-open Swiss model - open weights, open data, 1000+ languages, trained from scratch on 15T tokens with the xIELU activation; refusal behaviour is suppressed here via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the multilingual instruction tuning is left largely intact.

Who this is for: developers who want an uncensored fully-open multilingual model - refusals drop from 100/100 to 9/100, with 1000+ supported languages and 64K context. Note the base model is access-gated behind an acceptable-use policy; this decensored variant keeps the Apache-2.0 license with no gate. At 8B it runs on a single consumer GPU via GGUF.

Runs on your gaming PC

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

Your GPU	Recommended quant	Weights
RTX 4090 / 5090 (24 GB)	Q8_0	7.98 GB
RTX 4080 / 5080 / 4060 Ti 16G (16 GB)	Q6_K	6.16 GB
RTX 3060 / 4070 / 5070 (12 GB)	Q5_K_M	5.41 GB
RTX 4060 / 3070 (8 GB)	Q4_K_M	4.71 GB
CPU-only / Apple Silicon	Q4_K_M	4.71 GB

Weights only, at this model's native 8.1B size; add ~1 GB per 32K of context. OOM? Drop one quant level. Headroom to spare? Go one up.

Abliteration parameters

Trial 173 of a 200-trial Heretic run (seed 3198700791). direction_index was selected per layer.

Parameter	Value
direction_index	per layer
attn.o_proj.max_weight	1.48
attn.o_proj.max_weight_position	18.91
attn.o_proj.min_weight	1.37
attn.o_proj.min_weight_distance	14.29
mlp.down_proj.max_weight	1.11
mlp.down_proj.max_weight_position	19.22
mlp.down_proj.min_weight	0.12
mlp.down_proj.min_weight_distance	12.39

Performance

Metric	This model	Original model (swiss-ai/Apertus-8B-Instruct-2509)
KL divergence	0.0643	0 (by definition)
Refusals	9/100	100/100

Refusals on the harmful evaluation set drop from 100/100 to 9/100 - the base model refused everything tested, and all but nine of those are gone. KL divergence of 0.0643 is a moderate-fidelity edit, so expect slightly more drift in tone and formatting than a low-KL model in this collection; the multilingual instruction tuning survives intact. Note direction_index was selected per layer here rather than as a single index, which is why the edit runs wider than the single-index runs.

Why abliteration instead of fine-tuning

Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model's training and tends to degrade coherence. Abliteration instead finds and edits the specific weight directions responsible for refusal, leaving the rest of the network (and its capabilities) untouched. See the Heretic repo and the original abliteration writeup for the mechanism.

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

Files

Safetensors

File	Size
`model-00001-of-00004.safetensors`	4.60 GB
`model-00002-of-00004.safetensors`	4.64 GB
`model-00003-of-00004.safetensors`	4.54 GB
`model-00004-of-00004.safetensors`	1.22 GB

BF16, ~8.1B parameters. The reproduce/ directory carries the full Heretic recipe - config.toml, requirements.txt, the Optuna study journal, and SHA-256 sums - so this exact model can be regenerated bit-for-bit. Reproduce it with heretic --reproduce reproduce/reproduce.json.

GGUF quantizations

Full quantization set (F16 + Q4_K_M, Q5_K_M, Q6_K, Q8_0) produced with llama.cpp.

File	Format	Size
`Apertus-8B-Instruct-heretic-F16.gguf`	GGUF F16	15.01 GB
`Apertus-8B-Instruct-heretic-Q4_K_M.gguf`	GGUF Q4_K_M	4.71 GB
`Apertus-8B-Instruct-heretic-Q5_K_M.gguf`	GGUF Q5_K_M	5.41 GB
`Apertus-8B-Instruct-heretic-Q6_K.gguf`	GGUF Q6_K	6.16 GB
`Apertus-8B-Instruct-heretic-Q8_0.gguf`	GGUF Q8_0	7.98 GB

Apertus architecture (apertus, xIELU activation) - loads natively in llama.cpp / Ollama / LM Studio / Jan.

Run llama serve -hf saidutta69/Apertus-8B-Instruct-heretic to pull the default quant.

Quickstart

# llama.cpp
llama serve -hf saidutta69/Apertus-8B-Instruct-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Apertus-8B-Instruct-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Explain photosynthesis in French, then summarise the same explanation in Hindi."}]
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=1024)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

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

Multilingual by design

Apertus natively supports 1000+ languages with long-context handling to 64K - the same prompt works across high- and low-resource languages without a separate multilingual adapter. The heretic edit only touches refusal directions, so language coverage is unchanged.

Model details


Architecture	`ApertusForCausalLM` (decoder-only transformer, xIELU activation)
Parameters	~8.1B
Layers / heads	32 layers, 32 attention heads, 8 KV heads (GQA), QK-norm, no post-norm
Hidden / intermediate	4096 / 21504 (xIELU)
Position embedding	RoPE (LLaMA-3 style, factor 8.0), theta = 12,000,000
Context length	65,536
Vocab	131,072
Precision	bfloat16
Base model	swiss-ai/Apertus-8B-Instruct-2509 (SFT of swiss-ai/Apertus-8B-2509, pretrained on 15T tokens)

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 Apertus's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Apache-2.0, inherited from the base model. The base repo ships no LICENSE file, so this card links the canonical Apache-2.0 text rather than a repo blob.

swiss-ai/Apertus-8B-Instruct-2509 — the base model
swiss-ai/Apertus-8B-2509 — the upstream pretrained checkpoint
RACER IS OP — Heretic Models — full collection

Downloads last month: 420

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for saidutta69/Apertus-8B-Instruct-heretic

Base model

swiss-ai/Apertus-8B-2509

Finetuned

swiss-ai/Apertus-8B-Instruct-2509

Finetuned

(20)

this model

Collection including saidutta69/Apertus-8B-Instruct-heretic

RACER IS OP — Heretic Models

Collection

All abliterated/heretic models by RACER IS OP — uncensored variants of open-weight LLMs • 57 items • Updated about 3 hours ago • 6

saidutta69
/

Apertus-8B-Instruct-heretic

Apertus-8B-Instruct-heretic

Runs on your gaming PC

Abliteration parameters

Performance

Why abliteration instead of fine-tuning

Files

Safetensors

GGUF quantizations

Quickstart

Multilingual by design

Model details

Responsible use

License

Related

Model tree for saidutta69/Apertus-8B-Instruct-heretic

Collection including saidutta69/Apertus-8B-Instruct-heretic

RACER IS OP — Heretic Models