stable-code:instruct

模型	大小	Python	C++	Javascript	Java	PHP	Rust
Stable Code	3B	32.4%	30.9%	32.1%	32.1%	24.2%	23.0%
CodeLLama	7B	30.0%	28.2%	32.5%	31.1%	25.7%	26.3%
Deepseek Coder	1.3B	28.6%	29.2%	28.7%	29.0%	23.6%	18.5%
Wizard Coder	3B	31.6%	25.6%	26.2%	25.8%	25.3%	20.4%
StarCoder	3B	21.6%	19.8%	21.5%	20.5%	19.0%	16.9%
Replit Code V1.5	3B	23.0%	25.9%	26.2%	23.6%	23.2%	21.5%
Deci Coder	1B	19.1%	6.8%	18.4%	16.7%	2.1%	1.7%

模型詳細資訊

開發者：Stability AI
模型類型：stable-code 模型是基於 transformer 解碼器架構的自迴歸語言模型。
語言：英文、程式碼
聯絡方式：若您對模型有任何問題或意見，請寄送電子郵件至 lm@stability.ai

模型架構

此模型是一個僅解碼器的 transformer，類似於 LLaMA (Touvron et al., 2023) 架構，但進行了以下修改

參數	隱藏層大小	層數	注意力頭	序列長度
2,796,431,360	2560	32	32	16384

位置嵌入：旋轉位置嵌入 (Su et al., 2021) 應用於前 25% 的注意力頭嵌入維度，以提高吞吐量，參照 Black et al. (2022)。
分詞器：我們使用 GPTNeoX 分詞器的修改版本。NeoX。我們添加了特殊符記來訓練填空中間 (FIM) 功能，例如 <FIM_PREFIX> 和 <FIM_SUFFIX> 以及其他特殊符記。

訓練

訓練資料集

此資料集包含過濾後的開放原始碼大型資料集混合，這些資料集可在 HuggingFace Hub 上取得：Falcon RefinedWeb 提取 (Penedo et al., 2023)，以及 CommitPackFT 和 the-stack-github-issues (BigCode., 2023) 和 StarCoder (Li et al., 2023)。我們進一步使用來自數學領域的資料來補充我們的訓練 (Azerbayev, Zhangir, et al., 2023 和 Yu, Longhui, et al., 2023)。

訓練時使用的前 18 種程式語言：- C - CPP - Java - JavaScript - CSS - Go - HTML - Ruby - Rust - Markdown - Shell - Php - Sql - R - Typescript - Python - Jupyter-Clean - RestructuredText

使用與限制

預期用途

此模型旨在作為應用程式特定微調的基礎模型使用。開發人員必須評估和微調模型，以確保在下游應用程式中的安全效能。

限制與偏見

作為基礎模型，此模型可能表現出不可靠、不安全或其他不良行為，必須在部署前透過評估和微調來修正。預訓練資料集可能包含冒犯性或不適當的內容 (即使在應用資料清理篩選器後)，這可能會反映在模型產生的文字中。我們建議使用者在生產系統中使用這些模型時要謹慎。如果模型不適合您的應用程式，或任何可能對他人造成蓄意或非蓄意傷害的應用程式，請勿使用這些模型。

參考文獻

Hugging Face

Stable Code 3B is a 3 billion parameter Large Language Model (LLM), allowing accurate and responsive code completion at a level on par with models such as Code Llama 7b that are 2.5x larger.

**Key Features**

* **NEW** instruct model `ollama run stable-code`
* Fill in Middle Capability (FIM)
* Supports Long Context, trained with Sequences upto 16,384

![spiderchart](https://github.com/jmorganca/ollama/assets/3325447/6c3de7a5-5e10-4884-81fb-3a1b3f566609)

| Model            | Size | Python | C++  | Javascript | Java | PHP  | Rust |
|------------------|------|--------|------|------------|------|------|------|
| **Stable Code**  | 3B   | 32.4%  | 30.9%| 32.1%      | 32.1%| 24.2%| 23.0%|
| CodeLLama        | 7B   | 30.0%  | 28.2%| 32.5%      | 31.1%| 25.7%| 26.3%|
| Deepseek Coder   | 1.3B | 28.6%  | 29.2%| 28.7%      | 29.0%| 23.6%| 18.5%|
| Wizard Coder     | 3B   | 31.6%  | 25.6%| 26.2%      | 25.8%| 25.3%| 20.4%|
| StarCoder        | 3B   | 21.6%  | 19.8%| 21.5%      | 20.5%| 19.0%| 16.9%|
| Replit Code V1.5 | 3B   | 23.0%  | 25.9%| 26.2%      | 23.6%| 23.2%| 21.5%|
| Deci Coder       | 1B   | 19.1%  | 6.8% | 18.4%      | 16.7%| 2.1% | 1.7% |

## Model Details

* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: stable-code models are auto-regressive language models based on the transformer decoder architecture.
* **Language(s)**: English, Code
* **Contact**: For questions and comments about the model, please email `lm@stability.ai`

### Model Architecture

The model is a decoder-only transformer similar to the LLaMA ([Touvron et al., 2023](https://arxiv.org/abs/2307.09288)) architecture with the following modifications:

| Parameters     | Hidden Size | Layers | Heads | Sequence Length |
|----------------|-------------|--------|-------|-----------------|
| 2,796,431,360  | 2560        | 32     | 32    | 16384            |

* **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf).
* **Tokenizer**: We use a modified version of the GPTNeoX Tokenizer.[`NeoX`](https://github.com/EleutherAI/gpt-neox). We add special tokens to train for Fill in the Middle (FIM) capabilities like `<FIM_PREFIX>` and `<FIM_SUFFIX>` along with other special tokens.

## Training

### Training Dataset

The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), along with [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) and [Github Issues](https://huggingface.co/datasets/bigcode/the-stack-github-issues) (BigCode., 2023), and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)). We further supplement our training with data from mathematical domains ([Azerbayev, Zhangir, et al., 2023](https://arxiv.org/abs/2310.10631) and, [Yu, Longhui, et al., 2023](https://arxiv.org/abs/2309.12284)).

Top 18 programming languages trained on:
- C
- CPP
- Java
- JavaScript
- CSS
- Go
- HTML
- Ruby
- Rust
- Markdown
- Shell
- Php
- Sql
- R
- Typescript
- Python
- Jupyter-Clean
- RestructuredText

## Use and Limitations

### Intended Use

The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications.

### Limitations and Bias

As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.

## References

[Hugging Face](https://huggingface.co/stabilityai/stable-code-3b)

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