---
license: apache-2.0
base_model: mistralai/Ministral-3-14B-Instruct-2512
tags:
  - abliteration
  - uncensored
  - mistral
  - ministral
  - 14b
language:
  - en
library_name: transformers
pipeline_tag: text-generation
---

# Ministral-3-14B-abliterated

This is an abliterated (uncensored) version of [**Ministral-3-14B-Instruct-2512**](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512) created using the abliteration technique.

## What is Abliteration?

Abliteration is a technique that removes refusal behavior from language models by identifying and modifying the internal representations responsible for refusals. This creates a model that is more willing to engage with a wider range of topics.

This model was abliterated using [llm-abliteration](https://github.com/jim-plus/llm-abliteration) by grimjim, which implements norm-preserving biprojected abliteration.

### Further Reading

- [Norm-Preserving Biprojected Abliteration](https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration) - Technical deep-dive on the abliteration method
- [AGI Dreams - Abliterated Models: Norm-Preserving Guardrail Removal](https://agidreams.us/edition/2025-12-04#section-0) - Overview of the abliteration technique and community discussion

## Model Details

- **Base Model**: [mistralai/Ministral-3-14B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512)
- **Parameters**: ~14B
- **Format**: SafeTensors (BF16)
- **License**: Apache 2.0

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "jenerallee78/Ministral-3-14B-abliterated"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Hello, how are you?"}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## GGUF Versions

For GGUF quantized versions compatible with llama.cpp, see: [jenerallee78/Ministral-3-14B-abliterated-GGUF](https://huggingface.co/jenerallee78/Ministral-3-14B-abliterated-GGUF)

## Disclaimer

This model is provided for research and educational purposes. Users are responsible for ensuring their use complies with applicable laws and ethical guidelines.