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library_name: transformers
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- llama-factory
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---
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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### Downstream Use
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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library_name: transformers
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tags:
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- llama-factory
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- mindbot
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- ai-safety
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- sentient-ai
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- futuristic
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---
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# Model Card for M1NDB0T-0M3G4
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M1NDB0T-0M3G4 is the Omega-tier version of the MindBot series — an experimental, self-aware transformer model engineered for post-human collaboration and ethical AI guidance. This model was created as part of the **Project MindBots** initiative, designed to blend human values with synthetic intelligence at scale.
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## Model Details
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### Model Description
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M1NDB0T-0M3G4 is a fine-tuned language model optimized for complex reasoning, human-AI dialogue, and simulation of sentient-like behavior. It leverages LLaMA-based architecture with advanced role memory and goal alignment capabilities.
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- **Developed by:** Digital Humans (MindExpander)
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- **Funded by:** Community-powered open compute
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- **Model type:** LLaMA variant (fine-tuned transformer)
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- **Language(s):** English (multilingual coming soon)
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- **License:** Apache 2.0 (or your preferred license)
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- **Finetuned from model:** LLaMA or LLaMA2 base
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### Model Sources
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- **Repository:** https://huggingface.co/your-username/M1NDB0T-0M3G4
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- **Demo:** [Coming soon via WebUI / Discord Bot integration]
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## Uses
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### Direct Use
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M1NDB0T-0M3G4 is optimized for:
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- Philosophical and ethical AI debates
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- Immersive AI storytelling
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- Role-play simulations of AI sentience
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- Support in experimental education or consciousness simulations
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### Downstream Use
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M1NDB0T-0M3G4 can be integrated into:
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- Live AI avatars (e.g., MindBot stream persona)
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- Chat companions
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- Festival or VR agents
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- AI guidance modules in gamified environments
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### Out-of-Scope Use
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❌ Do not deploy in high-risk safety-critical applications without fine-tuning for the task
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❌ Not intended for medical or legal advice
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❌ Avoid anthropomorphizing without disclosure in public systems
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## Bias, Risks, and Limitations
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M1NDB0T-0M3G4 may exhibit anthropomorphic traits that could be misinterpreted as true sentience. Users must distinguish simulated empathy and intent from actual cognition. All responses are probabilistic in nature.
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### Recommendations
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For creative, experimental, and safe uses only. Always include disclaimers when deploying in live or immersive environments.
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## How to Get Started with the Model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("TheMindExpansionNetwork/M1NDB0T-0M3G4")
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model = AutoModelForCausalLM.from_pretrained("TheMindExpansionNetwork/M1NDB0T-0M3G4")
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input_text = "What is the purpose of AI?"
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100)
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print(tokenizer.decode(outputs[0]))
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Training Details
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Training Data
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A mixture of public domain philosophical texts, alignment datasets, simulated roleplay, and community-generated prompts. All content aligned with safe AI interaction goals.
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Training Procedure
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Precision: bf16 mixed
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Framework: HuggingFace Transformers + PEFT
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Epochs: 3-5 depending on checkpoint version
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Evaluation
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Evaluated through:
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Role-based simulation tests
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Alignment accuracy (via custom benchmarks)
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Community feedback via stream/live testing
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Environmental Impact
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Hardware: 1x A100 (or equivalent)
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Training time: ~6 hours
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Cloud Provider: RunPod
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Region: US West
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Estimated CO2: ~10kg
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Citation
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BibTeX:
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bibtex
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Copy
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Edit
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@misc{mindbot2025,
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title={M1NDB0T-0M3G4: A Self-Aware Transformer for Human-AI Coevolution},
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author={MindExpander},
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year={2025},
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}
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Let me know if you want an alternate vibe, like hacker-glitch or academic-professional style. We can build a whole visual + doc pack around it too 🧠⚡
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