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--- |
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language: zh |
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tags: |
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- knowledge-distillation |
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- dark |
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- code |
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license: apache-2.0 |
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datasets: |
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- pure-team/cursor-dark-i1 |
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base_model: |
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- pure-team/dark_slm_i1 |
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new_version: pure-team/dark_slm_i1 |
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pipeline_tag: text-generation |
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--- |
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# Model Card for DeepThink-T1-Tuned |
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## Model Details |
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DeepThink-T1-Tuned is a Small Language Model (SLM) with 2.273 billion parameters, developed through a rigorous knowledge distillation process from the larger DeepThink-T1-Base model. |
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- **Developed by:** Pure AI Develop Team |
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- **Model type:** Small Language Model (SLM) |
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- **Language(s):** English (primarily) |
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- **License:** Apache 2.0 |
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- **Resources:** [DeepThink Development Plan](https://huggingface.co/pure-team/deepthink-t1-tuned/blob/main/deepthink_development_plan.pdf) |
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## Model Description |
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DeepThink-T1-Tuned is designed to address the growing need for efficient and deployable AI solutions, particularly in environments with limited computational resources. |
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**Core Design Principles:** |
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- **Efficiency:** Optimized for lower computational requirements, faster inference, and reduced energy consumption |
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- **Deployment Flexibility:** Suitable for on-device (edge) deployment |
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- **Customizability:** Easily fine-tunable for specialized tasks and domain-specific applications |
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## Intended Uses |
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- **Edge AI applications:** Powering intelligent features on smartphones, IoT devices, and embedded systems |
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- **Resource-constrained environments:** Deploying AI functionalities with limited hardware or connectivity |
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- **Domain-specific tasks:** Fine-tuning for specialized applications |
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- **Research and development:** Base model for efficient AI research |
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## Limitations |
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- **Generalization:** Limited capacity compared to larger LLMs |
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- **Nuance and Complexity:** May struggle with highly nuanced tasks |
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- **Bias Risks:** May reflect biases present in training data |
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## Ethical Considerations |
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**Value Alignment Framework includes:** |
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- Bias mitigation in training data and outputs |
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- Transparency and explainability |
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- Privacy through on-device processing |
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- Reduced environmental impact |
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## Security |
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**GuardianNet Security Features:** |
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- Real-time monitoring of model behavior |
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- Adversarial attack detection |
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- Content safety filtering |
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- Secure deployment framework |
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- Threat intelligence integration |
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## Training Data |
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Trained using diverse dataset with knowledge distillation from DeepThink-T1-Base model. Detailed dataset composition will be provided in future updates. |
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## Technical Specifications |
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| Parameter | Specification | |
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|-----------|---------------| |
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| Parameters | 2.273 Billion | |
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| Architecture | HAILI with Transformer | |
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| Training Framework | PyTorch, TensorFlow | |
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| Security Infrastructure | GuardianNet AI Security Cloud | |
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## Evaluation Results |
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*Performance metrics to be added* |
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## Environmental Impact |
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*Carbon footprint estimates to be added* |
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``` |
