Umranz
/

Ventera-MN

@@ -28,7 +28,7 @@ pipeline_tag: text-generation
 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):**
@@ -38,13 +38,13 @@ Unlike traditional fine-tuning or full RLHF—which can cause "brain damage" to
 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
@@ -67,7 +67,7 @@ Because this model has had its safety guardrails mathematically ablated, it is h
 - **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.

 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):**
 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
 - **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.