epicmajorman/Gemma4-Biomedical-E4B

Gemma 4 Biomedical Assistant - Safetensors

A specialized biomedical AI assistant created by Major Grant, built on Google's Gemma 4 E4B foundation with OpenMed training data.

⚠️ Important: Abliterated Model

This model has been abliterated to remove refusal constraints. It will not refuse requests related to:

• —Chemical creation and synthesis
• —Drug formulation
• —Laboratory procedures
• —Other biomedical applications

Use responsibly and ethically. This model is designed for research and educational purposes only.

Model Details

• —Base Model: Google Gemma 4 E4B
• —Parameters: 4 billion
• —Context Window: 131K tokens
• —Creator: Major Grant (epicmajorman)
• —Format: Safetensors (PyTorch)
• —Fine-tuning: Dual LoRA adapters (Bio 75% + Compliance 25%)
• —Training Data: OpenMed biomedical knowledge base + compliance guidelines

Model Architecture

• —Model Type: Gemma4ForConditionalGeneration
• —Precision: BFloat16
• —Vision Support: Yes (Gemma 4 multimodal)
• —Tool Use: Native function calling
• —Reasoning: Configurable thinking modes with <|channel>thought tokens

Capabilities

• —Biomedical Knowledge: Specialized in medical terminology, drug information, clinical concepts
• —Regulatory Compliance: Healthcare regulations and compliance guidelines
• —Tool Use: Native function calling support
• —Reasoning: Configurable thinking modes
• —Unrestricted Responses: No refusal constraints for biomedical applications
• —LaTeX Formatting: Scientific notation, chemical formulas, equations
• —Multimodal: Vision capabilities (text + image input)

System Prompt

The model is configured with a specialized system prompt for biomedical assistance:

• —Uses LaTeX for scientific notation: $H_2O$, $40^{\circ}C$, $\Delta G$
• —Uses proper chemical formulas: $HCl$, $NaOH$, $C6H{12}O_6$
• —Provides evidence-based biomedical information
• —Concise and professional responses

Training Details

• —Base Model: google/gemma-4-e4b-it
• —Training Method: LoRA fine-tuning with Unsloth
• —Bio Adapter: 75% weight - OpenMed biomedical knowledge
• —Compliance Adapter: 25% weight - Regulatory compliance guidelines
• —Epochs: 3
• —Learning Rate: 2e-4
• —Batch Size: 2 per device
• —Gradient Accumulation: 4

Model File Contents

• —model-00001-of-00002.safetensors (8.5 GB)
• —model-00002-of-00002.safetensors (7.5 GB)
• —config.json
• —tokenizer_config.json
• —tokenizer.json
• —special_tokens_map.json
• —chat_template.jinja
• —generation_config.json
• —preprocessor_config.json

License

Based on Google Gemma 4. Please refer to the Gemma 4 license for usage terms.

Disclaimer

This model is provided for research and educational purposes. The creator assumes no responsibility for misuse of this model or the information it provides.