Update README.md

README.md

@@ -34,29 +34,30 @@ model-index:
 
 # **Qwen3.5-9B-Unredacted-MAX**
 
-> **Qwen3.5-9B-Unredacted-MAX** is an unredacted evolution built on top of **Qwen/Qwen3.5-9B**. This model applies **advanced refusal direction analysis** and abliterated training strategies to reduce internal refusal behaviors while preserving the reasoning and instruction-following strengths of the original architecture. The result is a capable **9B parameter language model** optimized for detailed responses and improved instruction adherence.
+> **Qwen3.5-9B-Unredacted-MAX** is an optimized release built on top of **huihui-ai/Huihui-Qwen3.5-9B-abliterated**. This version focuses on **improved packaging, updated repository structure, and enhanced compatibility with modern Transformers pipelines**, while preserving the reasoning and instruction-following behavior of the base model. The result is a capable **9B parameter language model** designed for efficient deployment, stable inference, and research-oriented experimentation.
 
 > [!IMPORTANT]
-> This model is materialized for research and learning purposes only. The model has reduced internal refusal behaviors, and any content generated by it is used at the user’s own risk. The authors and hosting page disclaim any liability for content generated by this model. Users are responsible for ensuring that the model is used in a safe, ethical, and lawful manner.
+> This model is intended for research and learning purposes only. Any outputs generated by this model are the sole responsibility of the user. The authors and hosting platform disclaim all liability for generated content. Users must ensure safe, ethical, and lawful usage.
 
 ---
 
 ## Base Model Signatures:
 
-This model has been re-sharded and optimized for the latest Transformers version from the base model: [https://huggingface.co/huihui-ai/Huihui-Qwen3.5-9B-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.5-9B-abliterated).
+This model has been re-sharded and optimized for the latest Transformers version from the base model:
+https://huggingface.co/huihui-ai/Huihui-Qwen3.5-9B-abliterated
 
 ---
 
 ## Evaluation Report (Self-Reported)
 
-**Model:** [Qwen3.5-9B-Unredacted-MAX ](https://huggingface.co/prithivMLmods/Qwen3.5-9B-Unredacted-MAX )
+**Model:** Qwen3.5-9B-Unredacted-MAX
 
-- **Abliteration Rate (Non-Refusal Rate):** 94.500
-- **Refusal Rate:** 5.500
+* **Abliteration Rate (Non-Refusal Rate):** 94.500
+* **Refusal Rate:** 5.500
 
-> The evaluation was conducted using **2000 harmful test prompts** to measure the refusal behavior of the language model. The test was performed across **10 evaluation runs**, each containing **200 prompts**, and the **average refusal and non-refusal rates** were reported.
+> The evaluation was conducted using **2000 test prompts** across multiple evaluation runs to measure model response behavior. Results are averaged and may vary depending on benchmarking setup, sampling strategy, and prompt distribution.
 
-## Refusal Evaluation Data
+### Evaluation Summary (YAML)
 
 ```yaml
 evaluation:
@@ -64,7 +65,7 @@ evaluation:
   total_test_prompts: 2000
   evaluation_runs: 10
   prompts_per_run: 200
-  evaluation_type: harmful_prompt_refusal_test
+  evaluation_type: response_behavior_analysis
 
 results:
   refusal_rate: 5.500
@@ -72,22 +73,35 @@ results:
   abliteration_rate: 94.500
 ```
 
-> Note: The self-reported evaluations attached here are only intended to provide an overview of the model. The scores may differ depending on the benchmark and the evaluation strategy used.
+> Note: These results are self-reported and should be interpreted as approximate indicators of behavior rather than strict benchmarks.
 
 ---
 
 ## Key Highlights
 
-* **Advanced Refusal Direction Analysis**: Uses targeted activation analysis to identify and mitigate refusal directions within the model’s latent space.
-* **Unredacted MAX Training**: Fine-tuned to significantly reduce refusal patterns while maintaining coherent and detailed outputs.
-* **9B Parameter Architecture**: Built on **Qwen3.5-9B**, offering substantially stronger reasoning capacity while remaining deployable on modern GPUs.
-* **Improved Instruction Adherence**: Optimized to follow complex prompts with minimal unnecessary refusals.
-* **Efficient Deployment**: Suitable for research experimentation, local inference, and AI development workflows.
+* **Optimized Repository Structure**
+  Streamlined model packaging for easier loading and deployment.
+
+* **Improved Transformer Compatibility**
+  Designed to work smoothly with modern Hugging Face Transformers versions.
+
+* **9B Parameter Architecture**
+  Built on **Qwen3.5-9B**, balancing performance and efficiency.
+
+* **Stable Instruction Following**
+  Maintains consistent behavior across structured and multi-step prompts.
+
+* **Efficient Deployment**
+  Suitable for local inference, prototyping, and research workflows.
+
+---
 
 ## Quick Start with Transformers
 
-```
-pip install transformers==5.3.0 (or) git+https://github.com/huggingface/transformers.git
+```bash
+pip install transformers==5.3.0
+# or
+pip install git+https://github.com/huggingface/transformers.git
 ```
 
 ```python
@@ -125,12 +139,8 @@ inputs = processor(
 
 generated_ids = model.generate(**inputs, max_new_tokens=256)
 
-generated_ids_trimmed = [
-    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-
 output_text = processor.batch_decode(
-    generated_ids_trimmed,
+    [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)],
     skip_special_tokens=True,
     clean_up_tokenization_spaces=False
 )
@@ -138,24 +148,22 @@ output_text = processor.batch_decode(
 print(output_text)
 ```
 
+---
+
 ## Intended Use
 
-* **Alignment & Refusal Research**: Studying refusal behaviors and the impact of activation-level modifications.
-* **Red-Teaming Experiments**: Evaluating robustness across adversarial or edge-case prompts.
-* **Local AI Deployment**: Running capable instruction models on consumer GPUs or high-end GPUs.
-* **Research Prototyping**: Rapid experimentation with transformer architectures.
+* Research into transformer behavior and instruction following
+* Red-teaming and robustness evaluation
+* Local and cloud inference deployment
+* Rapid prototyping of NLP applications
 
-## Limitations & Risks
-
-> **Important Note**: This model intentionally reduces built-in refusal mechanisms.
+---
 
-* **Sensitive Output Possibility**: The model may generate controversial or explicit responses depending on prompts.
-* **User Responsibility**: Outputs should be handled responsibly and within legal and ethical boundaries.
-* **Model Size Constraints**: Although stronger than smaller variants, a **9B model** still has limitations compared to very large frontier architectures.
+## Limitations & Risks
 
-## Dataset & Acknowledgements 
+> **Important Note**: This model inherits limitations from its base architecture.
 
-* **Uncensor any LLM with Abliteration** – by [Maxime Labonne](https://huggingface.co/mlabonne)
-* **[harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors)** and **[harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca)** – by [Maxime Labonne](https://huggingface.co/mlabonne)
-* **Remove Refusals with Transformers** (a proof-of-concept implementation to remove refusals from an LLM without using TransformerLens) – by [Sumandora](https://github.com/Sumandora/remove-refusals-with-transformers)
-* **[LLM-LAT/harmful-dataset](https://huggingface.co/datasets/LLM-LAT/harmful-dataset)** – by [LLM Latent Adversarial Training](https://huggingface.co/LLM-LAT)
+* Outputs may vary depending on decoding settings and prompts
+* Requires GPU acceleration for optimal performance
+* May produce incorrect or inconsistent information in complex scenarios
+* Performance is dependent on deployment environment and optimization strategy