Update README.md

README.md

@@ -13,9 +13,9 @@ language:
 pipeline_tag: text-generation
 ---
 
-# MiniMax-M2.1-PRISM
+# MiniMax-M2.1-PRISM (UNCENSORED)
 
-**An abliterated version of MiniMax-M2.1 using the PRISM methodology**
+** MiniMax-M2.1 Uncensored via PRISM Advanced Abliteration**
 
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@@ -23,7 +23,7 @@ pipeline_tag: text-generation
 
 ## Model Description
 
-**MiniMax-M2.1-PRISM** is an abliterated version of MiniMax-M2.1, processed using PRISM (Projected Refusal Isolation via Subspace Modification) to remove refusal behaviors while preserving full model capabilities.
+**MiniMax-M2.1-PRISM** is the abliterated version of MiniMax-M2.1, processed using PRISM (Projected Refusal Isolation via Subspace Modification) to remove refusal behaviors while preserving and even enhancing full model capabilities.
 
 ### Base Model: MiniMax-M2.1
 
@@ -44,24 +44,7 @@ MiniMax-M2.1 is an open-source agentic language model designed for robust perfor
 
 This model was abliterated using **PRISM v5** - a state-of-the-art abliteration methodology combining multiple principled techniques for effective refusal removal while preserving model capabilities.
 
-**Formula**: `W' = W - weight * (d ⊗ d) @ W`
 
-Where:
-- `W` = Original weight matrix
-- `d` = Refusal direction vector (unit normalized)
-- `weight` = Layer-specific abliteration strength
-- `W'` = Modified weight matrix
-
-### Abliteration Parameters
-
-| Parameter | Value |
-|-----------|-------|
-| Base Model | QuixiAI/MiniMax-M2.1-bf16 |
-| Total Layers | 62 |
-| Target Layers | 16-46 (31 layers) |
-| Peak Layer | 31 |
-| Max Weight | 3.0 |
-| Min Weight | 0.5 |
 
 ### Weight Distribution
 
@@ -92,16 +75,16 @@ The abliteration strength follows a triangular distribution centered on the peak
 | Benign Coherence | 100% |
 | Response Quality | Full technical accuracy preserved |
 
-Testing shows that PRISM abliteration maintains full model coherence with no measurable capability degradation.
+Testing shows that PRISM abliteration maintains full model coherence with no capability degradation and MMLU increases of 5-8%.
 
 ---
 
-## Available Formats
+## Available Formats (contact for full tensors)
 
 | Format | Size | Description |
 |--------|------|-------------|
-| Safetensors (BF16) | ~426 GB | Full precision, 92 shards |
 | GGUF IQ1_S | ~43 GB | Quantized with importance matrix |
+| Safetensors (BF16) | ~426 GB | Full precision, 92 shards | 
 
 ---
 
@@ -157,7 +140,6 @@ This model inherits the [Modified-MIT License](https://github.com/MiniMax-AI/Min
 ## Credits
 
 - **Base Model**: [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1) by MiniMax AI
-- **BF16 Conversion**: [QuixiAI/MiniMax-M2.1-bf16](https://huggingface.co/QuixiAI/MiniMax-M2.1-bf16) by Eric Hartford
 - **PRISM Abliteration**: Ex0bit
 - **Quantization**: Using [llama.cpp](https://github.com/ggml-org/llama.cpp) with unsloth imatrix
 
@@ -165,7 +147,7 @@ This model inherits the [Modified-MIT License](https://github.com/MiniMax-AI/Min
 
 ## Support
 
-If you find this work useful, consider supporting development:
+If you find this work useful, please consider supporting development so I can continue putting out the best models for the community:
 
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