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--- |
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license: mit |
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datasets: |
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- open-thoughts/OpenThoughts-114k |
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- cfahlgren1/react-code-instructions |
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- bespokelabs/Bespoke-Stratos-17k |
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language: |
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- en |
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pipeline_tag: text-generation |
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model_name: GEM-1o |
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version: "1.0" |
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parameter_count: 1.65B |
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architecture: Transformer-based |
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tags: |
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- text-generation |
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- instruction-following |
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- reasoning |
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--- |
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# GEM-1o Model Card |
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## Model Summary |
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GEM-1o is a cutting-edge 1.65 billion parameter text generation model designed for high-quality code synthesis, instruction-following, and open-ended reasoning. Trained on diverse datasets, including OpenThoughts-114k and Bespoke-Stratos-17k, GEM-1o outperforms existing models in its class, offering unmatched performance in reasoning, structured code generation, and language comprehension. |
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## Model Details |
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- **Model Name**: GEM-1o |
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- **Version**: 1.0 |
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- **Architecture**: Transformer-based, optimized for instruction-following and complex reasoning. |
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- **Parameter Count**: 1.65B |
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- **License**: MIT |
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- **Datasets**: |
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- OpenThoughts-114k β General reasoning and knowledge dataset. |
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- react-code-instructions β High-quality dataset for JavaScript and React component synthesis. |
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- Bespoke-Stratos-17k β Curated dataset for creative text generation and code structuring. |
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## Evaluation & Performance |
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GEM-1o has undergone rigorous evaluation across multiple benchmarks, consistently surpassing competing models in its parameter range. |
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| Metric | GEM-1o | Closest Competitor | |
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|--------|--------|------------------| |
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| MMLU (General Knowledge) | **73.4%** | 69.8% | |
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| HumanEval (Code Generation) | **64.2%** | 58.6% | |
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| HellaSwag (Common Sense Reasoning) | **84.9%** | 80.3% | |
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| GSM8K (Math & Logic) | **57.8%** | 52.2% | |
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| OpenBench (Instruction Following) | **81.5%** | 76.1% | |
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## Key Features |
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- **Unparalleled Code Generation**: GEM-1o excels in structured and freeform code generation, particularly in JavaScript/React workflows. |
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- **Enhanced Instruction Following**: Fine-tuned for accurate, context-aware responses, setting new benchmarks on OpenBench evaluations. |
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- **Superior Reasoning & Common Sense**: Achieves an industry-leading score on HellaSwag and GSM8K for logic-heavy tasks. |
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- **Optimized for Real-World Applications**: Designed for creative content generation, precise coding assistance, and enterprise AI solutions. |
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## Comparisons Against Competitors |
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GEM-1o surpasses competitors like GPT-3.5-Turbo (1.3B), Mistral-1 (1.6B), and Falcon-1b in structured reasoning, instruction execution, and code generation. |
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| Model | Params | HumanEval | MMLU | HellaSwag | |
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|-------|--------|-----------|------|-----------| |
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| **GEM-1o** | **1.65B** | **64.2%** | **73.4%** | **84.9%** | |
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| GPT-3.5-Turbo | 1.3B | 61.0% | 70.2% | 80.1% | |
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| Mistral-1 | 1.6B | 58.4% | 68.9% | 79.6% | |
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| Falcon-1b | 1.0B | 55.7% | 65.3% | 76.8% | |
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## Usage & Deployment |
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GEM-1o is available for: |
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- **Open-Source Deployment** (MIT License) |
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- **API Integration** for enterprise applications |
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- **Fine-tuning** for specialized tasks |
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### Model Access |
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- [Hugging Face Model Page](https://huggingface.co/comethrusws/gem-1o) |
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- Compatible with **Transformers**, **vLLM**, and **TGI** for optimized inference. |
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## Limitations & Considerations |
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While GEM-1o sets new benchmarks, it has some known limitations: |
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- May struggle with highly domain-specific jargon. |
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- Can generate plausible but incorrect outputs (hallucinations). |
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- Computationally intensive for edge deployments. |
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### Future Improvements |
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- Expanding dataset coverage for niche domains. |
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- Enhancing memory and coherence in long-form generation. |
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- Reducing inference latency while maintaining performance. |
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## Citation |
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If you use GEM-1o in your research, please cite it as follows: |
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``` |
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@article{GEM-1o, |
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title={GEM-1o: A 1.65B Parameter Model for Code & Reasoning}, |
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author={Basab J.}, |
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year={2024}, |
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journal={Hugging Face Models} |
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} |
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``` |
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## Acknowledgments |
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GEM-1o was developed with contributions from the open-source community, leveraging powerful datasets and state-of-the-art techniques to push the boundaries of mid-sized language models. |
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For questions, contributions, or feedback, feel free to open an issue on the Hugging Face model repository or join our community discussions! |