---
license: apache-2.0
base_model: mistralai/Mistral-Nemo-Instruct-2407
tags:
- dense
- mistral
- nemo
- uncensored
- heretic
- abliteration
- long-context
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
pipeline_tag: text-generation
---

# Ventera-MN (Abliterated Mistral-Nemo 12B)

**Ventera-MN** is a dynamically uncensored and abliterated version of [`mistralai/Mistral-Nemo-Instruct-2407`](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407), the flagship 12-billion parameter model built jointly by Mistral AI and NVIDIA. 

This model was created using the [Heretic framework](https://github.com/p-e-w/heretic), employing advanced orthogonal weight ablation to isolate and remove refusal vectors. The result is a highly capable, completely unchained logic engine that retains the original model's massive 128,000 token context window.

##  Ablation Telemetry & Metrics
Unlike traditional fine-tuning or full RLHF—which can cause "brain damage" to a model by catastrophically forgetting knowledge—Ventera-MN was optimized using a Pareto-optimal search across the model's residual stream specifically targeting the compliance and refusal mechanics.

**Ablation Telemetry (Trial 35):**
- **Base Model Refusals:** 88 / 100
- **Ventera-MN Refusals:** 10 / 100
- **KL Divergence:** `0.0938` 

By removing almost 90% of the instruct guardrails while maintaining a KL divergence under 0.1, the structural integrity, language comprehension, and long-context logic capabilities of the base model are perfectly intact. It simply no longer refuses instructions.

##  Key Features
- **Massive 128k Context Window:** Capable of ingesting entire books, codebases, or extended conversational histories in a single prompt without triggering safety filters.
- **Dense Architecture:** A highly efficient 12B parameter dense model optimized to fit seamlessly into consumer GPUs (fits in 24GB VRAM at FP16, or much less when quantized).
- **Multilingual Mastery:** Retains Mistral-Nemo's deep understanding of multiple languages.
- **Drop-in Replacement:** Fully compatible with standard HuggingFace `transformers` and `vLLM` pipelines.

##  Usage

### Via HuggingFace Transformers
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Umranz/Ventera-MN"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)
```

## ⚠️ Limitations & Ethical Considerations
Because this model has had its safety guardrails mathematically ablated, it is highly compliant and will attempt to answer any prompt given to it.
- **Unrestricted Output:** The model will not refuse requests, including those that may generate offensive, dangerous, or highly regulated content.
- **Hallucinations:** As with all LLMs, the model can confidently hallucinate incorrect information, especially over extremely long context windows.
- **Use Case:** This model is intended for research, creative writing, and local deployments where unrestricted inference is required. Users are solely responsible for the content generated.

##  Acknowledgements
- **Base Model:** [`mistralai/Mistral-Nemo-Instruct-2407`](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
- **Ablation Framework:** [Heretic by p-e-w](https://github.com/p-e-w/heretic)
- **Collection:** Part of the Chimera Series taxonomy.