--- 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. [](https://github.com/unslothai/unsloth)