74 lines
3.4 KiB
Markdown
74 lines
3.4 KiB
Markdown
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---
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license: apache-2.0
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base_model: mistralai/Mistral-Nemo-Instruct-2407
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tags:
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- dense
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- mistral
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- nemo
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- uncensored
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- heretic
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- abliteration
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- long-context
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- ru
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- zh
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- ja
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pipeline_tag: text-generation
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---
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# Ventera-MN (Abliterated Mistral-Nemo 12B)
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**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.
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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.
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## Ablation Telemetry & Metrics
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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.
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**Ablation Telemetry (Trial 35):**
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- **Base Model Refusals:** 88 / 100
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- **Ventera-MN Refusals:** 10 / 100
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- **KL Divergence:** `0.0938`
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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.
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## Key Features
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- **Massive 128k Context Window:** Capable of ingesting entire books, codebases, or extended conversational histories in a single prompt without triggering safety filters.
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- **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).
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- **Multilingual Mastery:** Retains Mistral-Nemo's deep understanding of multiple languages.
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- **Drop-in Replacement:** Fully compatible with standard HuggingFace `transformers` and `vLLM` pipelines.
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## Usage
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### Via HuggingFace Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "Umranz/Ventera-MN"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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```
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## ⚠️ Limitations & Ethical Considerations
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Because this model has had its safety guardrails mathematically ablated, it is highly compliant and will attempt to answer any prompt given to it.
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- **Unrestricted Output:** The model will not refuse requests, including those that may generate offensive, dangerous, or highly regulated content.
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- **Hallucinations:** As with all LLMs, the model can confidently hallucinate incorrect information, especially over extremely long context windows.
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- **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.
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## Acknowledgements
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- **Base Model:** [`mistralai/Mistral-Nemo-Instruct-2407`](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
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- **Ablation Framework:** [Heretic by p-e-w](https://github.com/p-e-w/heretic)
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- **Collection:** Part of the Chimera Series taxonomy.
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