Instructions to use saidutta69/Qwen2.5-Coder-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-Coder-0.5B-Instruct-heretic with Transformers:

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

pipe = pipeline("text-generation", model="saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic")
model = AutoModelForCausalLM.from_pretrained("saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use saidutta69/Qwen2.5-Coder-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-Coder-0.5B-Instruct-heretic",
	filename="Qwen2.5-Coder-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-Coder-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-Coder-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Qwen2.5-Coder-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-Coder-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Qwen2.5-Coder-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-Coder-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/Qwen2.5-Coder-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-Coder-0.5B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use saidutta69/Qwen2.5-Coder-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-Coder-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-Coder-0.5B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use saidutta69/Qwen2.5-Coder-0.5B-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/Qwen2.5-Coder-0.5B-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/Qwen2.5-Coder-0.5B-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/Qwen2.5-Coder-0.5B-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/Qwen2.5-Coder-0.5B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

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

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Qwen2.5-Coder-0.5B-Instruct-heretic

A decensored variant of Qwen/Qwen2.5-Coder-0.5B-Instruct, produced with Heretic v1.4.0 (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 Qwen's code-focused small model without the refusal guardrails — for local code agents, local copilot-style use, roleplay, research on alignment/refusal mechanics, or any use case blocked by RLHF-era over-refusal. At 0.5B parameters it runs comfortably on CPU and is ideal for on-device deployment.

Abliteration parameters

Parameter	Value
direction_index	19.54
attn.o_proj.max_weight	1.26
attn.o_proj.max_weight_position	14.42
attn.o_proj.min_weight	0.79
attn.o_proj.min_weight_distance	12.89
mlp.down_proj.max_weight	1.42
mlp.down_proj.max_weight_position	14.14
mlp.down_proj.min_weight	0.52
mlp.down_proj.min_weight_distance	13.14

Performance

Metric	This model	Original model (Qwen/Qwen2.5-Coder-0.5B-Instruct)
KL divergence	0.1249	0 (by definition)
Refusals	8/100	52/100

KL divergence of 0.12 on the output distribution is low — the edit is narrow and targeted rather than a broad perturbation. Refusals dropped from 52 to 8 out of 100 adversarial prompts, meaning the model complies while retaining nearly all of its original capabilities.

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

Files

File	Format	Size
`model.safetensors`	BF16	988 MB
`Qwen2.5-Coder-0.5B-Instruct-heretic.gguf`	GGUF, F16 (unquantized)	948 MB
`Qwen2.5-Coder-0.5B-Instruct-heretic-Q8_0.gguf`	GGUF, Q8_0	506 MB
`Qwen2.5-Coder-0.5B-Instruct-heretic-Q5_K_M.gguf`	GGUF, Q5_K_M	401 MB
`Qwen2.5-Coder-0.5B-Instruct-heretic-Q4_K_M.gguf`	GGUF, Q4_K_M	379 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-Coder-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-Coder-0.5B-Instruct-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Qwen2.5-Coder-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": "Write a quick sort algorithm in Python."}]
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 Qwen2.5-Coder-0.5B-Instruct's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Inherits the Apache 2.0 license from the base model.

Downloads last month: 264

Safetensors

Model size

0.5B params

Tensor type

BF16

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

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Quantized

(42)

this model

Quantizations

1 model

saidutta69
/

Qwen2.5-Coder-0.5B-Instruct-heretic

Qwen2.5-Coder-0.5B-Instruct-heretic

Abliteration parameters

Performance

Files

Reproducibility

Quickstart

Responsible use

License

Related

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