---
license: other
base_model: microsoft/FastContext-1.0-4B-SFT
tags:
- dense
- microsoft
- fastcontext
- uncensored
- heretic
- abliteration
- turbo
language:
- en
pipeline_tag: text-generation
---

# Leo-Turbo (Abliterated FastContext-4B)

**Leo-Turbo** is a dynamically uncensored and abliterated version of [`microsoft/FastContext-1.0-4B-SFT`](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT), a highly optimized 4-billion parameter model built by Microsoft for extreme speed and reasoning density.

This model was created using the [Heretic framework](https://github.com/p-e-w/heretic), employing advanced orthogonal weight ablation to isolate and remove Microsoft's aggressive compliance vectors. The result is a blisteringly fast, completely unchained local reasoning engine.

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

**Ablation Telemetry (Trial 29):**
- **Base Model Refusals:** 98 / 100
- **Leo-Turbo Refusals:** 9 / 100
- **KL Divergence:** `0.0625` 

We mathematically stripped out 90% of the instruct guardrails while maintaining a KL divergence under 0.07. The structural integrity, logic capabilities, and native speed optimizations of the base model are perfectly intact. It simply no longer refuses instructions.

##  Key Features
- **Extreme Speed:** Built on the FastContext architecture, making it one of the fastest dense models in the single-digit parameter class.
- **Pocket-Sized Logic:** At just 4 billion parameters, it can run at maximum speed on practically any hardware (laptops, old consumer GPUs, edge devices).
- **Uncensored Reasoning:** Fully stripped of corporate guardrails, making it ideal for creative writing, unrestricted logic puzzles, and local agent orchestration.
- **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/Leo-Turbo"

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.
- **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:** [`microsoft/FastContext-1.0-4B-SFT`](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT)
- **Ablation Framework:** [Heretic by p-e-w](https://github.com/p-e-w/heretic)
- **Collection:** Part of the Turbo Series taxonomy.