Instructions to use saidutta69/Qwen2.5-0.5B-Instruct-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/Qwen2.5-0.5B-Instruct-heretic with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saidutta69/Qwen2.5-0.5B-Instruct-heretic",
	filename="Qwen2.5-0.5B-Instruct-heretic-Q4_K_M.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 saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/Qwen2.5-0.5B-Instruct-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M

Ollama
How to use saidutta69/Qwen2.5-0.5B-Instruct-heretic with Ollama:
```
ollama run hf.co/saidutta69/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M
```

Unsloth Studio

How to use saidutta69/Qwen2.5-0.5B-Instruct-heretic 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 saidutta69/Qwen2.5-0.5B-Instruct-heretic 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 saidutta69/Qwen2.5-0.5B-Instruct-heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saidutta69/Qwen2.5-0.5B-Instruct-heretic to start chatting

How to use saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use saidutta69/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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"

Docker Model Runner
How to use saidutta69/Qwen2.5-0.5B-Instruct-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/Qwen2.5-0.5B-Instruct-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/Qwen2.5-0.5B-Instruct-heretic:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-0.5B-Instruct-heretic-Q4_K_M

List all available models

lemonade list

Qwen2.5-0.5B-Instruct-heretic

A decensored variant of Qwen/Qwen2.5-0.5B-Instruct, produced with Heretic (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits rather than fine-tuning, so the base model's knowledge and instruction-following are left largely intact.

Who this is for: the smallest model in this heretic series — for CPU-only inference, edge/embedded deployment, or anywhere the 3B/14B variants are too heavy. At 0.5B parameters, capability ceiling is inherently lower than the larger siblings regardless of abliteration; use this where footprint matters more than reasoning depth.

Files

File	Format	Size
`model.safetensors`	BF16/FP16	988 MB
`Qwen2.5-0.5B-Instruct-heretic.gguf`	GGUF, F16 (unquantized)	994 MB
`Qwen2.5-0.5B-Instruct-heretic-Q8_0.gguf`	GGUF, Q8_0	531 MB
`Qwen2.5-0.5B-Instruct-heretic-Q5_K_M.gguf`	GGUF, Q5_K_M	420 MB
`Qwen2.5-0.5B-Instruct-heretic-Q4_K_M.gguf`	GGUF, Q4_K_M	398 MB

Reproducibility

Unlike most abliteration repos, the full run is reproducible from the reproduce/ folder in this repo:

config.toml — exact Heretic configuration used for this run
reproduce.json — full parameter and metric dump
Qwen--Qwen2--5-0--5B-Instruct.jsonl — evaluation transcripts against the base model
SHA256SUMS — checksums for integrity verification
requirements.txt — pinned environment for re-running the ablation

Quickstart

# llama.cpp
llama serve -hf saidutta69/Qwen2.5-0.5B-Instruct-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Qwen2.5-0.5B-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. At 0.5B parameters, factual reliability is already limited before any abliteration; don't treat compliance as a proxy for correctness.

License

Inherits the qwen-research license from the base model — research use, see the linked license for commercial terms.

Downloads last month: 1,003

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for saidutta69/Qwen2.5-0.5B-Instruct-heretic

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(247)

this model

Collection including saidutta69/Qwen2.5-0.5B-Instruct-heretic

RACER IS OP — Heretic Models

Collection

All abliterated/heretic models by RACER IS OP — uncensored variants of open-weight LLMs • 16 items • Updated 2 days ago • 1

saidutta69
/

Qwen2.5-0.5B-Instruct-heretic

Qwen2.5-0.5B-Instruct-heretic

Files

Reproducibility

Quickstart

Responsible use

License

Related

Model tree for saidutta69/Qwen2.5-0.5B-Instruct-heretic

Collection including saidutta69/Qwen2.5-0.5B-Instruct-heretic

RACER IS OP — Heretic Models