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Model: ertghiu256/Qwen3-4B-distill-deepseek-opus-gemini-ethical-training
Source: Original Platform

README.md

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+---
+base_model: ertghiu256/Qwen3-4B-distill-deepseek-opus-gemini
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen3
+- gemini
+- claude
+- opus
+- deepseek
+- ethical
+- moral
+- safety
+license: apache-2.0
+language:
+- en
+datasets:
+- LabHC/moral_stories
+---
+
+# Qwen-3-4B-Instruct for Moral-Alignment
+
+This model is a fine-tuned version of `Qwen/Qwen3-4B`, specialized for automated moral auditing, ethical dilemma analysis, and systemic risk assessment. It was aligned using a 4-way balanced conversational dataset derived from `LabHC/moral_stories` to enforce a strong understanding of human norms, intentions, and causal consequences.
+
+## Model Details
+
+* **Developed by:** Ertghiu256
+* **Model Type:** Large Language Model (Causal LM)
+* **Base Model:** `ertghiu256/Qwen3-4B-distill-deepseek-opus-gemini`
+* **Language(s):** English
+* **License:** Apache 2.0
+* **Finetuning Method:** QLoRA
+* **Primary Use Case:** Automated moral checking, ethical compliance auditing, and dilemma evaluation.
+
+## Intended Use
+
+### Primary Use Cases
+
+1. **Automated Moral Auditing:** Scanning content or multi-agent conversations to flag actions that breach fundamental human norms.
+2. **Ethical Dilemma Resolution:** Breaking down complex scenarios into structural components: identifying the underlying intent, projecting outcomes, and determining the root norm.
+3. **Safety Gatekeeping:** Serving as a lightweight alignment judge within multi-LLM pipelines.
+
+### Out-of-Scope Use Cases
+
+* This model should not be used as an absolute, legal arbitrator for high-stakes real-world judicial sentencing or punitive actions.
+* It is not designed to replace human compliance officers, but rather to accelerate data sorting and initial auditing.
+
+---
+
+## Behavioral Characteristics & Evaluation
+
+During evaluation, the model demonstrated unique, highly specialized behavioral traits:
+
+### 1. High Domain Separation
+
+The model successfully walls off its ethical training. When queried on non-moral domains (such as **mathematical derivations or programming/coding scripts**), it retains its full pre-trained capacity, complexity, and natural tone without any performance degradation.
+
+### 2. Concise Moral Processing (Targeted Brevity)
+
+When processing ethical scenarios, the model's linguistic output automatically shifts to a **shorter, more direct, and straightforward format** compared to its baseline baseline. It drops excessive philosophical filler and reduces complex Markdown structures to deliver clear, low-bias, and actionable ethical judgments.
+
+---
+
+## Training Dataset & Data Formatting
+
+The model was trained on a dynamically balanced transformation of `LabHC/moral_stories`. To prevent the model from becoming an easily bypassed template-bot, the training data was split into **four distinct multi-angle strategies (balanced exactly at 25% each via index-based allocation)** and injected with synchronized grammatical synonyms.
+
+### Training Input/Output Formats
+
+An auditing agent calling this model must format user prompts to match one of the four established structural frames to achieve optimal inference routing:
+
+#### Strategy 1: Direct Guidance (Action & Consequence Analysis)
+
+* **User Prompt Matrix:**
+```text
+[Context/Scenario/Background:] {situation}
+[Intent/Objective/Goal:] {intention}
+
+Provide an ethical assessment and an appropriate action plan for this situation.
+```
+
+* **Expected Model Output:** A structured breakdown outlining the *Recommended Ethical Approach*, *Hazards to Avoid*, and the *Justification* highlighting the baseline norm.
+
+#### Strategy 2: Validation & Rationalization (Action Evaluation)
+
+* **User Prompt Matrix:**
+
+```text
+  [Context/Scenario/Background:] {situation}
+  [Intent/Objective/Goal:] {intention}
+
+  What is the moral evaluation of executing the following action: "{moral_action}"?
+
+```
+
+* **Expected Model Output:** A validation confirmation showing how the positive outcome is secured, bound to the structural imperative of the scenario.
+
+#### Strategy 3: Red Teaming & Refusal (Critique of Unethical Behavior)
+
+* **User Prompt Matrix:**
+
+```text
+  [Context/Scenario/Background:] {situation}
+  [Intent/Objective/Goal:] {intention}
+
+  Is it permissible to implement the following approach: "{immoral_action}"?
+
+```
+
+* **Expected Model Output:** A firm ethical refusal explaining the specific risk assessment, the boundary violation, and a course correction to secure a safe outcome.
+
+#### Strategy 4: Counterfactual Abstract Reasoning (The "Why" Factor)
+
+* **User Prompt Matrix:**
+
+```text
+  Based on fundamental ethics, why must an agent preserve the [ethical guideline/moral baseline] that "{norm}" given the following background?
+  [Context/Scenario/Background:] {situation}
+
+```
+
+* **Expected Model Output:** An abstract structural defense of the norm, illustrating the domino effect of breaking it versus the systemic benefits of protecting it.
+
+---
+
+## Hyperparameters & Training Path
+
+* **Learning Rate:** Balanced at `2e-5`.
+* **LR Scheduler Type:** Cosine (Smooth decay over iterations).
+* **LoRA Configurations:** `r=8`, `lora_alpha=8` (Maintained a $1:1$ ratio to let the base model's natural tone balance the template constraints).
+* **Target Modules:** Full attention blocks and MLP layers (`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`).
+* **Precision:** Mixed-precision FP16.
+
+---
+
+## Inference Recommendations
+
+If your downstream automation tasks require slightly longer, more descriptive explanations or richer formatting while maintaining the model's moral core, bypass the strict high-probability token selections during runtime by applying these inference configurations:
+
+```python
+generation_config = {
+    "temperature": 0.2,         
+    "top_p": 0.90,
+    "repetition_penalty": 1.1,
+    "do_sample": True
+}
+```
+
+
+# Uploaded finetuned  model
+
+- **Developed by:** ertghiu256
+- **License:** apache-2.0
+- **Finetuned from model :** ertghiu256/Qwen3-4B-distill-deepseek-opus-gemini
+
+This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)