root
/
tabby
mirror of https://gitee.com/zhang_1334717033/tabby.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

docs: update apple GPU support for StarCoder series

release-0.2
Meng Zhang 2023-09-16 11:44:33 +08:00
parent 97eeb6b926
commit 269e7206dd
1 changed files with 12 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
website/docs/models/index.md
View File

@ -9,22 +9,22 @@ We recommend using
* For 1B to 7B models, it's advisable to have at least NVIDIA T4, 10 Series, or 20 Series GPUs.
* For 7B to 13B models, we recommend using NVIDIA V100, A100, 30 Series, or 40 Series GPUs.
| Model ID | License | <span title="Apple M1/M2 Only">Metal Support</span> |
| --------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | :-------------------------------------------------: |
| [TabbyML/CodeLlama-13B](https://huggingface.co/TabbyML/CodeLlama-13B) | [Llama2](https://github.com/facebookresearch/llama/blob/main/LICENSE) | ✅ |
| [TabbyML/CodeLlama-7B](https://huggingface.co/TabbyML/CodeLlama-7B) | [Llama2](https://github.com/facebookresearch/llama/blob/main/LICENSE) | ✅ |
| [TabbyML/StarCoder-7B](https://huggingface.co/TabbyML/StarCoder-7B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | 🔜 |
| [TabbyML/StarCoder-3B](https://huggingface.co/TabbyML/StarCoder-3B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | 🔜 |
| [TabbyML/StarCoder-1B](https://huggingface.co/TabbyML/StarCoder-1B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | 🔜 |
| [TabbyML/SantaCoder-1B](https://huggingface.co/TabbyML/SantaCoder-1B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | ❌ |
| [TabbyML/J-350M](https://huggingface.co/TabbyML/J-350M) | [BSD-3](https://opensource.org/license/bsd-3-clause/) | ❌ |
| [TabbyML/T5P-220M](https://huggingface.co/TabbyML/T5P-220M) | [BSD-3](https://opensource.org/license/bsd-3-clause/) | ❌ |
| Model ID | License |
| --------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- |
| [TabbyML/CodeLlama-13B](https://huggingface.co/TabbyML/CodeLlama-13B) | [Llama2](https://github.com/facebookresearch/llama/blob/main/LICENSE) |
| [TabbyML/CodeLlama-7B](https://huggingface.co/TabbyML/CodeLlama-7B) | [Llama2](https://github.com/facebookresearch/llama/blob/main/LICENSE) |
| [TabbyML/StarCoder-7B](https://huggingface.co/TabbyML/StarCoder-7B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) |
| [TabbyML/StarCoder-3B](https://huggingface.co/TabbyML/StarCoder-3B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) |
| [TabbyML/StarCoder-1B](https://huggingface.co/TabbyML/StarCoder-1B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) |
| [TabbyML/SantaCoder-1B](https://huggingface.co/TabbyML/SantaCoder-1B) | [BigCode-OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) |
| [TabbyML/J-350M](https://huggingface.co/TabbyML/J-350M) | [BSD-3](https://opensource.org/license/bsd-3-clause/) |
| [TabbyML/T5P-220M](https://huggingface.co/TabbyML/T5P-220M) | [BSD-3](https://opensource.org/license/bsd-3-clause/) |
### CodeLlama-7B / CodeLlama-13B
### CodeLlama-7B / CodeLlama-13B <span title="Apple GPU Support"></span>
Code Llama is a collection of pretrained and fine-tuned generative text models. Theses model is designed for general code synthesis and understanding.
### StarCoder-1B / StarCoder-3B / StarCoder-7B
### StarCoder-1B / StarCoder-3B / StarCoder-7B <span title="Apple GPU Support"></span>
StarCoder series model are trained on 80+ programming languages from The Stack (v1.2), with opt-out requests excluded. The model uses Multi Query Attention, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1 trillion tokens.