stable-code:3b-instruct-q4_K_S

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%

模型详情

开发方: Stability AI
模型类型: stable-code 模型是基于 transformer 解码器架构的自回归语言模型。
语言: 英语, 代码
联系方式: 如有关于模型的问题或意见，请发送邮件至 [email protected]

模型架构

该模型是一个仅解码器的 Transformer，类似于 LLaMA (Touvron et al., 2023) 架构，并进行了以下修改

参数	隐藏层大小	层数	头数	序列长度
2,796,431,360	2560	32	32	16384

位置嵌入: 旋转位置嵌入 (Su et al., 2021) 应用于头部嵌入维度的前 25%，以提高吞吐量，遵循 Black et al. (2022)。
分词器: 我们使用了 GPTNeoX 分词器的修改版本。NeoX。我们添加了特殊 tokens 以训练中间填充 (FIM) 能力，例如 <FIM_PREFIX> 和 <FIM_SUFFIX> 以及其他特殊 tokens。

训练

训练数据集

数据集由 HuggingFace Hub 上提供的开源大型数据集的过滤混合物组成：Falcon RefinedWeb extract (Penedo et al., 2023)、CommitPackFT 和 Github Issues (CommitPackFT 和 Github Issues) (BigCode., 2023) 以及 StarCoder (Li et al., 2023)。我们还使用来自数学领域的数据 (Azerbayev, Zhangir, et al., 2023 和 Yu, Longhui, et al., 2023) 进一步补充了我们的训练。

训练的前 18 种编程语言：- C - CPP - Java - JavaScript - CSS - Go - HTML - Ruby - Rust - Markdown - Shell - Php - Sql - R - Typescript - Python - Jupyter-Clean - RestructuredText

使用与限制

预期用途

该模型旨在用作应用程序特定微调的基础模型。开发人员必须评估和微调模型，以确保在下游应用程序中的安全性能。

限制与偏见

作为基础模型，该模型可能表现出不可靠、不安全或其他不良行为，必须在部署前通过评估和微调进行纠正。预训练数据集可能包含冒犯性或不适当的内容，即使在应用数据清理过滤器后，这些内容也可能反映在模型生成的文本中。我们建议用户在生产系统中使用这些模型时要谨慎。如果模型不适合您的应用程序，或者任何可能对他人造成有意或无意伤害的应用程序，请勿使用这些模型。

参考

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](http://hugging-face.cn/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](http://hugging-face.cn/datasets/tiiuae/falcon-refinedweb)), along with [CommitPackFT](http://hugging-face.cn/datasets/bigcode/commitpackft) and [Github Issues](http://hugging-face.cn/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](http://hugging-face.cn/stabilityai/stable-code-3b)

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