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README.md
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## Model Details
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- **Developed by:** Deci
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- **Model type:** DeciCoder is an auto-regressive language model based on the transformer decoder architecture, using Grouped Query Attention.
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- **Language(s):** Python, Java, JavaScript
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- **License:** Model checkpoints are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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| Infery LLM | 3,889.3 | 11,676.8 |
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- Throughput (tokens/sec) - Measured with optimal batch size per hardware - A10 on BS 128, A100 on BS 512
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- Infery-LLM, Deci's optimization and inference SDK's features a suite of optimization techniques, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To explore the full capabilities of Infery-LLM, we invite you to [book a demo](https://deci.ai/infery-llm-book-a-demo/) with our experts.
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## Documentation
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- [Notebook](https://colab.research.google.com/drive/1JCxvBsWCZKHfIcHSMVf7GZCs3ClMQPjs)
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- Blog post: [Introducing DeciCoder: The New Gold Standard in Efficient and Accurate Code Generation](https://deci.ai/blog/decicoder-efficient-and-accurate-code-generation-llm/)
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- Questions:Feel free to contact us via our [Discord Community!](https://discord.com/invite/p9ecgRhDR8/)
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## How to Cite
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## Model Details
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- **Developed by:** [Deci](https://deci.ai/)
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- **Model type:** DeciCoder is an auto-regressive language model based on the transformer decoder architecture, using Grouped Query Attention.
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- **Language(s):** Python, Java, JavaScript
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- **License:** Model checkpoints are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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| Infery LLM | 3,889.3 | 11,676.8 |
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- Throughput (tokens/sec) - Measured with optimal batch size per hardware - A10 on BS 128, A100 on BS 512
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- Infery-LLM, Deci's optimization and inference SDK's features a suite of optimization techniques, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To explore the full capabilities of Infery-LLM, we invite you to [book a demo](https://deci.ai/infery-llm-book-a-demo/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decicoder-1b) with our experts.
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## Documentation
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- [Notebook](https://colab.research.google.com/drive/1JCxvBsWCZKHfIcHSMVf7GZCs3ClMQPjs)
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- Blog post: [Introducing DeciCoder: The New Gold Standard in Efficient and Accurate Code Generation](https://deci.ai/blog/decicoder-efficient-and-accurate-code-generation-llm/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decicoder-1b)
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- Questions:Feel free to contact us via our [Discord Community!](https://discord.com/invite/p9ecgRhDR8/)
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## How to Cite
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