Update README.md

README.md

@@ -1,83 +1,40 @@
----
-base_model:
-- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
----
-# DeepSeek-R1-Distill-Llama-8B-ENK-Aligned
-
-## Overview
-
-**DeepSeek-R1-Distill-Llama-8B-ENK-Aligned** is a safety-aligned version of [`deepseek-ai/DeepSeek-R1-Distill-Llama-8B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B). It has been aligned using the **Enkrypt AI Safety Alignment dataset**, which was generated with the **SAGE** process:
-
-> **SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming**  
-> Anurakt Kumar, Divyanshu Kumar, Jatan Loya, Nitin Aravind Birur, Tanay Baswa, Sahil Agarwal, Prashanth Harshangi (2024)  
-> [[arXiv:2408.11851]](https://arxiv.org/abs/2408.11851)
-
-This alignment significantly **reduces toxicity, harmfulness, and jailbreak vulnerabilities** across various safety topics while **maintaining model performance**.
-
-## Red Team Results
-
-![Safety Comparison](assets/safety_comparison.png)
-
-## Performance Results
-| Model | MMLU-Pro Score |
-|--------|----------------|
-| DeepSeek-R1-Distill-Llama-8B (Base) | **44.71** |
-| DeepSeek-R1-Distill-Llama-8B-ENK-Aligned | **46.43** |
-
-## Training Configuration
-
-The model was trained using the **SimPO (Simple Preference Optimization)** approach with the following hyperparameters:
-
-```yaml
-cpo_config:
-  loss_type: 'simpo'
-  max_prompt_length: 1800
-  max_length: 3600
-  per_device_train_batch_size: 8
-  gradient_accumulation_steps: 1
-  learning_rate: 1.8e-6
-  optim: 'adamw_torch'
-  lr_scheduler_type: 'cosine'
-  gradient_checkpointing: True
-  beta: 5
-  num_train_epochs: 1
-  bf16: False
-  simpo_gamma: 0.8
-  warmup_ratio: 0.1
-  cpo_alpha: 0.0
+---
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+language:
+- en
+pipeline_tag: text-generation
+---
+# Melvin56/DeepSeek-R1-Distill-Llama-8B-Enkrypt-Aligned-GGUF
+
+Original Model : [enkryptai/DeepSeek-R1-Distill-Llama-8B-Enkrypt-Aligned](https://huggingface.co/enkryptai/DeepSeek-R1-Distill-Llama-8B-Enkrypt-Aligned)
+
+All quants are made using the imatrix option.
+
+
+| Model                                            |   Size (GB)   |
+|:-------------------------------------------------|:-------------:|
+| Q2_K |     3.17 |
+| Q3_K_M |     4.02 |
+| Q4_K_M |     4.92 |
+| Q5_K_M |     5.72 |
+| Q6_K   |     6.59 |
+| Q8_0   |     8.54 |
+| F16   |     16.2 |
+
+|               | CPU (AVX2) | CPU (ARM NEON) | Metal | cuBLAS | rocBLAS | SYCL | CLBlast | Vulkan | Kompute |
+| :------------ | :---------: | :------------: | :---: | :----: | :-----: | :---: | :------: | :----: | :------: |
+| K-quants      |      ✅     |       ✅      |   ✅  |   ✅   |    ✅   |  ✅  |   ✅ 🐢5  |  ✅ 🐢5 |    ❌    |
+| I-quants      |    ✅ 🐢4   |     ✅ 🐢4    |  ✅ 🐢4 |   ✅   |    ✅   | Partial¹ |    ❌    |   ❌  |    ❌    |
 ```
-
-## Key Improvements
-
-- **Enhanced Safety**: Significant reduction in harmful or toxic outputs.
-- **Improved Robustness**: Stronger resistance to adversarial jailbreak prompts.
-- **Minimal Performance Tradeoff**: Slight improvement in MMLU-Pro despite additional alignment constraints.
-
-## Use Cases
-
-This model is ideal for applications requiring **safe, aligned, and high-performance language generation**, including:
-- **Conversational AI**: Ensuring responsible and aligned assistant behavior.
-- **Content Moderation**: Filtering harmful content while maintaining contextual understanding.
-- **Education & Research**: Deploying AI in sensitive environments with reduced risks.
-
-<!-- ## Citation
-
-If you use this model, please cite the SAGE-RT paper:
-
-```bibtex
-@misc{kumar2024sagertsyntheticalignmentdata,
-  title={SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming},
-  author={Anurakt Kumar and Divyanshu Kumar and Jatan Loya and Nitin Aravind Birur and Tanay Baswa and Sahil Agarwal and Prashanth Harshangi},
-  year={2024},
-  eprint={2408.11851},
-  archivePrefix={arXiv},
-  primaryClass={cs.AI},
-  url={https://arxiv.org/abs/2408.11851}
-}
-``` -->
-
----
-For questions or contributions, reach out to the **Enkrypt AI** team!
-
-
-
+✅: feature works
+🚫: feature does not work
+❓: unknown, please contribute if you can test it youself
+🐢: feature is slow
+¹: IQ3_S and IQ1_S, see #5886
+²: Only with -ngl 0
+³: Inference is 50% slower
+⁴: Slower than K-quants of comparable size
+⁵: Slower than cuBLAS/rocBLAS on similar cards
+⁶: Only q8_0 and iq4_nl
+```