File size: 2,969 Bytes
212b319 a7d92df 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 ab8aeb7 0a217f5 ab8aeb7 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 212b319 0a217f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
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
## 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*
``` |