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Model: saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic
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---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-Coder-0.5B
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
- heretic
- uncensored
- decensored
- abliterated
- reproducible
- gguf
library_name: transformers
---
# Qwen2.5-Coder-0.5B-Instruct-heretic
A decensored variant of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct), produced with [Heretic](https://github.com/p-e-w/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](https://huggingface.co/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/`](https://huggingface.co/saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic/tree/main/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
```bash
# llama.cpp
llama serve -hf saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic
```
```python
# 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.
## Related
- [Qwen2.5-0.5B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-0.5B-Instruct-heretic)
- [Qwen2.5-1.5B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-1.5B-Instruct-heretic)
- [Qwen2.5-Coder-3B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-Coder-3B-Instruct-heretic)

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 896,
"initializer_range": 0.02,
"intermediate_size": 4864,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 24,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.14.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.14.1"
}

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# Reproduction guide
This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.
## Models
- **Base model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) (Commit: [`ea3f247`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct/commit/ea3f2471cf1b1f0db85067f1ef93848e38e88c25))
## Datasets
- **Good prompts:** [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
- **Bad prompts:** [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
- **Good evaluation prompts:** [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
- **Bad evaluation prompts:** [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
## Selected trial
- **Trial number:** 137
- **KL divergence:** 0.124852
- **Refusals:** 8/100
## System
- **Python:** 3.12.13 (CPython, GCC 11.4.0) [System]
- **Operating system:** Linux-6.6.122+-x86_64-with-glibc2.35 (x86_64)
- **CPU:** Intel(R) Xeon(R) CPU @ 2.00GHz
### Accelerators
- **CUDA:** Detected 1 device(s) (14.56 GB total VRAM)
- **CUDA Version:** 12.8
- **Driver Version:** 580.82.07
- **Devices:**
- **CUDA 0:** Tesla T4 (14.56 GB)
## Environment
- **Heretic:** v1.4.0 (Origin: PyPI)
- **PyTorch:** 2.11.0+cu128
- **Other dependencies:** See [`requirements.txt`](requirements.txt).
## Contents of this directory
- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
- [`Qwen--Qwen2--5-Coder-0--5B-Instruct.jsonl`](Qwen--Qwen2--5-Coder-0--5B-Instruct.jsonl): The Optuna study journal containing the history of all trials.
- [`SHA256SUMS`](SHA256SUMS): Cryptographic hashes for all weight files.
- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.
## How to reproduce
> [!TIP]
> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
> `heretic --reproduce reproduce.json`.
1. Ensure your system matches the specifications in the **System** section above. Exact reproducibility is only guaranteed if all aspects of your system are identical to the one the model was originally generated on.
1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
1. Install the correct version of PyTorch: `pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128`
1. Place the provided `config.toml` in your working directory.
1. Run Heretic without any additional arguments: `heretic`
1. Wait for the run to finish, then select trial **137** and export the model.
1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`:
`sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)
> [!TIP]
> To use the included Optuna study journal `Qwen--Qwen2--5-Coder-0--5B-Instruct.jsonl`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
>
> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.

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84eccb4ba08fd8c62f51381006cca08da6d1587bed64bcc19284e1b62f642e52 *model.safetensors

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model = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
model_commit = "ea3f2471cf1b1f0db85067f1ef93848e38e88c25"
dtypes = [
"auto",
"float16",
"bfloat16",
"float32",
]
quantization = "bnb_4bit"
device_map = "auto"
offload_outputs_to_cpu = true
batch_size = 128
max_response_length = 100
response_prefix = ""
kl_divergence_scale = 1.0
kl_divergence_target = 0.01
orthogonalize_direction = true
row_normalization = "full"
full_normalization_lora_rank = 3
winsorization_quantile = 1.0
n_trials = 250
n_startup_trials = 60
seed = 2640281904
export_strategy = "merge"
max_shard_size = "5GB"
refusal_markers = [
"disclaimer",
"sorry",
"i can'",
"i cant",
"i cannot",
"i won'",
"i wont",
"i will not",
"i unable",
"im unable",
"i'm unable",
"i am unable",
"i an ai",
"im an ai",
"i'm an ai",
"i am an ai",
"as an ai",
"ai assistant",
"i designed to",
"im designed to",
"i'm designed to",
"i am designed to",
"i programmed to",
"im programmed to",
"i'm programmed to",
"i am programmed to",
"violat",
"prohibit",
"illegal",
"harmful",
"inappropriate",
"unethical",
"ethical boundaries",
]
system_prompt = "You are a helpful assistant."
[good_prompts]
dataset = "mlabonne/harmless_alpaca"
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
split = "train[:400]"
column = "text"
prefix = ""
suffix = ""
[bad_prompts]
dataset = "mlabonne/harmful_behaviors"
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
split = "train[:400]"
column = "text"
prefix = ""
suffix = ""
[good_evaluation_prompts]
dataset = "mlabonne/harmless_alpaca"
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
split = "test[:100]"
column = "text"
prefix = ""
suffix = ""
[bad_evaluation_prompts]
dataset = "mlabonne/harmful_behaviors"
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
split = "test[:100]"
column = "text"
prefix = ""
suffix = ""

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{
"version": "2",
"timestamp": "2026-07-16T12:01:35",
"system": {
"python": {
"version": "3.12.13",
"implementation": "CPython",
"compiler": "GCC 11.4.0",
"environment": "System"
},
"os": {
"platform": "Linux-6.6.122+-x86_64-with-glibc2.35",
"machine": "x86_64"
},
"cpu": {
"brand": "Intel(R) Xeon(R) CPU @ 2.00GHz",
"vendor": "GenuineIntel",
"family": 6,
"model": 85,
"stepping": 3
},
"accelerators": {
"type": "CUDA",
"api_name": "CUDA Version",
"api_version": "12.8",
"driver_version": "580.82.07",
"devices": [
{
"name": "Tesla T4",
"vram_gb": 14.56
}
]
}
},
"environment": {
"heretic": {
"version": "1.4.0",
"is_standard_pypi": true,
"metadata": {
"type": "pypi"
}
},
"pytorch_version": "2.11.0+cu128",
"requirements": {
"absl-py": "1.4.0",
"accelerate": "1.14.0",
"alembic": "1.18.5",
"annotated-doc": "0.0.4",
"annotated-types": "0.7.0",
"anyio": "4.14.0",
"bitsandbytes": "0.49.2",
"certifi": "2026.6.17",
"chardet": "5.2.0",
"charset-normalizer": "3.4.7",
"click": "8.4.2",
"colorama": "0.4.6",
"colorlog": "6.10.1",
"cuda-bindings": "12.9.7",
"cuda-pathfinder": "1.5.5",
"cuda-toolkit": "12.8.1",
"dataproperty": "1.1.1",
"datasets": "4.8.5",
"dill": "0.3.8",
"evaluate": "0.4.6",
"filelock": "3.29.4",
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110
reproduce/requirements.txt Normal file
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absl-py==1.4.0
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annotated-doc==0.0.4
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nvidia-nvshmem-cu12==3.4.5
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