Replete-Adapted-llama3-8b是一个通用模型,在超过100种编程语言中进行特殊训练以进行编码。

8B

88 Pulls 更新于6周前

6周前

0400303b8bfa · 6.1GB

model
llama
·
8.03B
·
Q5_1
license
META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Meta Llama 3” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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template
{{ if .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|>
params
{"num_keep":24,"stop":["<|start_header_id|>","<|end_header_id|>","<|eot_id|>"]}

README

Replete-AI/Llama3-8B-Instruct-Replete-Adapted

  • fp32 进行量化
  • 使用 i-matrix calibration_datav3.txt
  • 强烈推荐使用 fp16 版本或更少量化的版本进行运行;该模型在 q4_0 已经随机表现得非常oston)

Replete-Coder-llama3-8b

由 Rombodawg微调

不仅仅是编程模型!

尽管 Replete-Coder 在编程方面具有惊人的能力,但它是由大量非编程数据训练的,完全清理且无审查。不仅仅是用来编程,可以满足你的所有需求!我们真正在尝试打造 GPT 杀手!
image/png

感谢 TensorDock 赞助 Replete-Coder-llama3-8b 和 Replete-Coder-Qwen2-1.5b
您可以在以下链接中查看其云计算租赁服务。
- https://tensordock.com


Replete-Coder-llama3-8b 是一个通用的模型,特别在100多种编程语言中进行编码训练。用于训练模型的数据包含25%的非编码指令数据,75%的编码指令数据,总计达3.9百万行,大约10亿个token,或7.27GB的指令数据。用于训练该模型的数据为100%无审查,然后在其训练之前进行完全的去重。

Replete-Coder 模型(包括 Replete-Coder-llama3-8b 和 Replete-Coder-Qwen2-1.5b)具有以下特点

  • 超过100种编程语言的先进编程能力
  • 先进的代码转换(语言之间)
  • 安全性和漏洞防御相关的编程能力
  • 通用用途
  • 无审查的使用
  • 函数调用
  • 高等数学的使用
  • 在低成本(8b)和移动(1.5b)平台上的使用

注意:Replete-Coder 系列模型的微调在8192个token的上下文中进行。超过此上下文窗口的性能无法保证。

image/png


以下为25%的非编码指令

以下为75%的针对特定编码的指令数据

这两个数据集被结合成最终的训练数据集,以下链接

### System:
{}

### Instruction:
{}

### Response:
{}

注意:系统提示在训练数据中有所不同,但最常用的一个是

Below is an instruction that describes a task, Write a response that appropriately completes the request.

结束token

<|endoftext|>

感谢社区对 Replete-AI/code_bagel_hermes-2.5 数据集的贡献。如果没有这么多成员自由开源他们的数据集供任何人使用,这个惊人的AI模型将不可能存在。

特别感谢 Teknium 对 Open-Hermes-2.5 数据集的贡献,以及 jondurbin 对 bagel 数据集及其 code_bagel 系列数据集的命名想法。您可以通过以下链接找到他们的 huggingface 账户

还要特别感谢 unsloth,它被视为 Replete-Coder 的主要训练方法。以下是他们 github,以及用于训练该模型的特殊 Replete-Ai 秘制酱(Unsloth + Qlora + Galore)colab 代码文档。