stable-code:3b-code-q3_K_M

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

开发商： Stability AI
模型类型： stable-code 模型是基于 Transformer 解码器架构的自动回归语言模型。
语言：英语，代码
联系方式：如有任何问题或意见，请发送电子邮件至 [email protected]

模型架构

该模型是一个类似于 LLaMA（Touvron 等人，2023 年）架构的仅解码器 Transformer，并进行了以下修改。

参数	隐藏大小	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 等人，2021 年）应用于前 25% 的头部嵌入维度，以遵循 Black 等人 (2022 年) 的方法提高吞吐量。
分词器：我们使用了改进版的 GPTNeoX 分词器。NeoX。我们添加了特殊标记来训练中间填充 (FIM) 能力，例如 <FIM_PREFIX> 和 <FIM_SUFFIX> 以及其他特殊标记。

训练

训练数据集

该数据集由 HuggingFace Hub 上可用的经过过滤的混合开源大型数据集组成：Falcon RefinedWeb 提取（Penedo 等人，2023 年），以及 CommitPackFT 和 Github Issues (BigCode，2023 年) 和 StarCoder（Li 等人，2023 年）。我们还使用来自数学领域的数据补充我们的训练（Azerbayev, Zhangir 等人，2023 年和 Yu, Longhui 等人，2023 年）。

训练的十大编程语言：C、C++、Java、JavaScript、CSS、Go、HTML、Ruby、Rust、Markdown、Shell、Php、Sql、R、Typescript、Python、Jupyter-Clean、RestructuredText

使用和局限性

预期用途

该模型旨在用作特定于应用程序的微调的基础模型。开发人员必须评估和微调模型，以确保其在下游应用程序中的安全性能。

局限性和偏差

作为基础模型，该模型可能会表现出不可靠、不安全或其他不良行为，必须通过评估和微调才能在部署前纠正这些行为。即使应用了数据清洗过滤器，预训练数据集也可能包含攻击性或不当内容，这可能会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谨慎行事。如果这些模型不适合您的应用程序，或者任何可能对他人造成故意或意外伤害的应用程序，请勿使用这些模型。

参考文献

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

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