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: $HCl$, $NaOH$, $C_6H_{12}O_6$
- 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.jsontokenizer_config.jsontokenizer.jsonspecial_tokens_map.jsonchat_template.jinjageneration_config.jsonpreprocessor_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.
