---
language: zh
tags:
- knowledge-distillation
- dark
- code
license: apache-2.0
datasets:
- pure-team/cursor-dark-i1
base_model:
- pure-team/dark_slm_i1
new_version: pure-team/dark_slm_i1
pipeline_tag: text-generation
---
# Model Card for DeepThink-T1-Tuned

![DeepThink-T1-Tuned](https://cdn-uploads.huggingface.co/production/uploads/684d4d30096c845d720fe12c/KcwHRJkCqoF9IGyFG6UkT.png)

## Model Details

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.

- **Developed by:** Pure AI Develop Team
- **Model type:** Small Language Model (SLM)
- **Language(s):** English (primarily)
- **License:** Apache 2.0
- **Resources:** [DeepThink Development Plan](https://huggingface.co/pure-team/deepthink-t1-tuned/blob/main/deepthink_development_plan.pdf)

## Model Description

DeepThink-T1-Tuned is designed to address the growing need for efficient and deployable AI solutions, particularly in environments with limited computational resources.

**Core Design Principles:**
- **Efficiency:** Optimized for lower computational requirements, faster inference, and reduced energy consumption
- **Deployment Flexibility:** Suitable for on-device (edge) deployment
- **Customizability:** Easily fine-tunable for specialized tasks and domain-specific applications

## Intended Uses

- **Edge AI applications:** Powering intelligent features on smartphones, IoT devices, and embedded systems
- **Resource-constrained environments:** Deploying AI functionalities with limited hardware or connectivity
- **Domain-specific tasks:** Fine-tuning for specialized applications
- **Research and development:** Base model for efficient AI research

## Limitations

- **Generalization:** Limited capacity compared to larger LLMs
- **Nuance and Complexity:** May struggle with highly nuanced tasks
- **Bias Risks:** May reflect biases present in training data

## Ethical Considerations

**Value Alignment Framework includes:**
- Bias mitigation in training data and outputs
- Transparency and explainability
- Privacy through on-device processing
- Reduced environmental impact

## Security

**GuardianNet Security Features:**
- Real-time monitoring of model behavior
- Adversarial attack detection
- Content safety filtering
- Secure deployment framework
- Threat intelligence integration

## Training Data

Trained using diverse dataset with knowledge distillation from DeepThink-T1-Base model. Detailed dataset composition will be provided in future updates.

## Technical Specifications

| Parameter | Specification |
|-----------|---------------|
| Parameters | 2.273 Billion |
| Architecture | HAILI with Transformer |
| Training Framework | PyTorch, TensorFlow |
| Security Infrastructure | GuardianNet AI Security Cloud |

## Evaluation Results
*Performance metrics to be added*

## Environmental Impact
*Carbon footprint estimates to be added*
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