初始化项目，由ModelHub XC社区提供模型

Model: Umranz/Ventera-MN
Source: Original Platform

README.md

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+---
+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.