Update README.md
Browse files
README.md
CHANGED
|
@@ -64,7 +64,7 @@ Search-based technology, AutoNAC.
|
|
| 64 |
|
| 65 |
## Model Details
|
| 66 |
|
| 67 |
-
- **Developed by:** Deci
|
| 68 |
- **Model type:** DeciCoder is an auto-regressive language model based on the transformer decoder architecture, using Grouped Query Attention.
|
| 69 |
- **Language(s):** Python, Java, JavaScript
|
| 70 |
- **License:** Model checkpoints are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
|
|
@@ -159,12 +159,12 @@ Below are DeciCoder's pass@1 on MultiPL HumanEval scores
|
|
| 159 |
| Infery LLM | 3,889.3 | 11,676.8 |
|
| 160 |
|
| 161 |
- Throughput (tokens/sec) - Measured with optimal batch size per hardware - A10 on BS 128, A100 on BS 512
|
| 162 |
-
- 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
|
| 163 |
|
| 164 |
## Documentation
|
| 165 |
|
| 166 |
- [Notebook](https://colab.research.google.com/drive/1JCxvBsWCZKHfIcHSMVf7GZCs3ClMQPjs)
|
| 167 |
-
- 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
|
| 168 |
- Questions:Feel free to contact us via our [Discord Community!](https://discord.com/invite/p9ecgRhDR8/)
|
| 169 |
|
| 170 |
## How to Cite
|
|
|
|
| 64 |
|
| 65 |
## Model Details
|
| 66 |
|
| 67 |
+
- **Developed by:** [Deci](https://deci.ai/)
|
| 68 |
- **Model type:** DeciCoder is an auto-regressive language model based on the transformer decoder architecture, using Grouped Query Attention.
|
| 69 |
- **Language(s):** Python, Java, JavaScript
|
| 70 |
- **License:** Model checkpoints are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
|
|
|
|
| 159 |
| Infery LLM | 3,889.3 | 11,676.8 |
|
| 160 |
|
| 161 |
- Throughput (tokens/sec) - Measured with optimal batch size per hardware - A10 on BS 128, A100 on BS 512
|
| 162 |
+
- 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.
|
| 163 |
|
| 164 |
## Documentation
|
| 165 |
|
| 166 |
- [Notebook](https://colab.research.google.com/drive/1JCxvBsWCZKHfIcHSMVf7GZCs3ClMQPjs)
|
| 167 |
+
- 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)
|
| 168 |
- Questions:Feel free to contact us via our [Discord Community!](https://discord.com/invite/p9ecgRhDR8/)
|
| 169 |
|
| 170 |
## How to Cite
|