首页下载资源人工智能大模型实战教程，从0手撸LLM

ZIP大模型实战教程，从0手撸LLM

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资源文件列表:

llms-from-scratch-cn-main.zip 大约有1112个文件
  1. llms-from-scratch-cn-main/
  2. __MACOSX/._llms-from-scratch-cn-main 212B
  3. llms-from-scratch-cn-main/Translated_Book/
  4. __MACOSX/llms-from-scratch-cn-main/._Translated_Book 212B
  5. llms-from-scratch-cn-main/images/
  6. __MACOSX/llms-from-scratch-cn-main/._images 212B
  7. llms-from-scratch-cn-main/README.md 12.86KB
  8. __MACOSX/llms-from-scratch-cn-main/._README.md 212B
  9. llms-from-scratch-cn-main/Model_Architecture_Discussions/
  10. __MACOSX/llms-from-scratch-cn-main/._Model_Architecture_Discussions 212B
  11. llms-from-scratch-cn-main/.gitignore 3.22KB
  12. __MACOSX/llms-from-scratch-cn-main/._.gitignore 212B
  13. llms-from-scratch-cn-main/Book/
  14. __MACOSX/llms-from-scratch-cn-main/._Book 212B
  15. llms-from-scratch-cn-main/Codes/
  16. __MACOSX/llms-from-scratch-cn-main/._Codes 212B
  17. llms-from-scratch-cn-main/LICENSE.txt 1.02KB
  18. __MACOSX/llms-from-scratch-cn-main/._LICENSE.txt 212B
  19. llms-from-scratch-cn-main/Translated_Book/ch01/
  20. __MACOSX/llms-from-scratch-cn-main/Translated_Book/._ch01 212B
  21. llms-from-scratch-cn-main/Translated_Book/ch04/
  22. __MACOSX/llms-from-scratch-cn-main/Translated_Book/._ch04 212B
  23. llms-from-scratch-cn-main/Translated_Book/ch03/
  24. __MACOSX/llms-from-scratch-cn-main/Translated_Book/._ch03 212B
  25. llms-from-scratch-cn-main/Translated_Book/img/
  26. __MACOSX/llms-from-scratch-cn-main/Translated_Book/._img 212B
  27. llms-from-scratch-cn-main/Translated_Book/ch02/
  28. __MACOSX/llms-from-scratch-cn-main/Translated_Book/._ch02 212B
  29. llms-from-scratch-cn-main/Translated_Book/ch05/
  30. __MACOSX/llms-from-scratch-cn-main/Translated_Book/._ch05 212B
  31. llms-from-scratch-cn-main/images/mental-model.jpg 173.65KB
  32. __MACOSX/llms-from-scratch-cn-main/images/._mental-model.jpg 212B
  33. llms-from-scratch-cn-main/images/cover.jpg 47.2KB
  34. __MACOSX/llms-from-scratch-cn-main/images/._cover.jpg 212B
  35. llms-from-scratch-cn-main/Model_Architecture_Discussions/llama3/
  36. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._llama3 212B
  37. llms-from-scratch-cn-main/Model_Architecture_Discussions/phi-3/
  38. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._phi-3 212B
  39. llms-from-scratch-cn-main/Model_Architecture_Discussions/olmo/
  40. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._olmo 212B
  41. llms-from-scratch-cn-main/Model_Architecture_Discussions/MiniCPM/
  42. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._MiniCPM 212B
  43. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-v1/
  44. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-v1 212B
  45. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-v6/
  46. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-v6 212B
  47. llms-from-scratch-cn-main/Model_Architecture_Discussions/pangu/
  48. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._pangu 212B
  49. llms-from-scratch-cn-main/Model_Architecture_Discussions/mamba/
  50. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._mamba 212B
  51. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-compare/
  52. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-compare 212B
  53. llms-from-scratch-cn-main/Model_Architecture_Discussions/.keep
  54. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._.keep 212B
  55. llms-from-scratch-cn-main/Model_Architecture_Discussions/ChatGLM4/
  56. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._ChatGLM4 212B
  57. llms-from-scratch-cn-main/Model_Architecture_Discussions/ChatGLM3/
  58. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._ChatGLM3 212B
  59. llms-from-scratch-cn-main/Model_Architecture_Discussions/img/
  60. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._img 212B
  61. llms-from-scratch-cn-main/Model_Architecture_Discussions/openelm/
  62. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._openelm 212B
  63. llms-from-scratch-cn-main/Model_Architecture_Discussions/gptj/
  64. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._gptj 212B
  65. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-v3/
  66. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-v3 212B
  67. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-v4/
  68. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-v4 212B
  69. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-v5/
  70. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-v5 212B
  71. llms-from-scratch-cn-main/Model_Architecture_Discussions/rwkv-v2/
  72. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._rwkv-v2 212B
  73. llms-from-scratch-cn-main/Model_Architecture_Discussions/phi/
  74. __MACOSX/llms-from-scratch-cn-main/Model_Architecture_Discussions/._phi 212B
  75. llms-from-scratch-cn-main/Book/ch06/
  76. __MACOSX/llms-from-scratch-cn-main/Book/._ch06 212B
  77. llms-from-scratch-cn-main/Book/ch01/
  78. __MACOSX/llms-from-scratch-cn-main/Book/._ch01 212B
  79. llms-from-scratch-cn-main/Book/ch04/
  80. __MACOSX/llms-from-scratch-cn-main/Book/._ch04 212B
  81. llms-from-scratch-cn-main/Book/ch03/
  82. __MACOSX/llms-from-scratch-cn-main/Book/._ch03 212B
  83. llms-from-scratch-cn-main/Book/ch02/
  84. __MACOSX/llms-from-scratch-cn-main/Book/._ch02 212B
  85. llms-from-scratch-cn-main/Book/ch05/
  86. __MACOSX/llms-from-scratch-cn-main/Book/._ch05 212B
  87. llms-from-scratch-cn-main/Codes/ch07/
  88. __MACOSX/llms-from-scratch-cn-main/Codes/._ch07 212B
  89. llms-from-scratch-cn-main/Codes/ch06/
  90. __MACOSX/llms-from-scratch-cn-main/Codes/._ch06 212B
  91. llms-from-scratch-cn-main/Codes/ch01/
  92. __MACOSX/llms-from-scratch-cn-main/Codes/._ch01 212B
  93. llms-from-scratch-cn-main/Codes/appendix-B/
  94. __MACOSX/llms-from-scratch-cn-main/Codes/._appendix-B 212B
  95. llms-from-scratch-cn-main/Codes/ch04/
  96. __MACOSX/llms-from-scratch-cn-main/Codes/._ch04 212B
  97. llms-from-scratch-cn-main/Codes/ch03/
  98. __MACOSX/llms-from-scratch-cn-main/Codes/._ch03 212B
  99. llms-from-scratch-cn-main/Codes/ch02/
  100. __MACOSX/llms-from-scratch-cn-main/Codes/._ch02 212B
  101. llms-from-scratch-cn-main/Codes/ch05/
  102. __MACOSX/llms-from-scratch-cn-main/Codes/._ch05 212B
  103. llms-from-scratch-cn-main/Codes/appendix-A/
  104. __MACOSX/llms-from-scratch-cn-main/Codes/._appendix-A 212B
  105. llms-from-scratch-cn-main/Translated_Book/ch01/1.1什么是LLM.md 20.69KB
  106. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.1什么是LLM.md 212B
  107. llms-from-scratch-cn-main/Translated_Book/ch01/1.0理解大型语言模型.md 14.14KB
  108. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.0理解大型语言模型.md 212B
  109. llms-from-scratch-cn-main/Translated_Book/ch01/1.8总结.ipynb 2.63KB
  110. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.8总结.ipynb 212B
  111. llms-from-scratch-cn-main/Translated_Book/ch01/.keep
  112. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._.keep 212B
  113. llms-from-scratch-cn-main/Translated_Book/ch01/1.6深入剖析GPT架构.ipynb 5.44KB
  114. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.6深入剖析GPT架构.ipynb 212B
  115. llms-from-scratch-cn-main/Translated_Book/ch01/1.7构建大语言模型.ipynb 2.47KB
  116. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.7构建大语言模型.ipynb 212B
  117. llms-from-scratch-cn-main/Translated_Book/ch01/1.2LLMs的应用.md 5.15KB
  118. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.2LLMs的应用.md 212B
  119. llms-from-scratch-cn-main/Translated_Book/ch01/1.5利用大型数据集.ipynb 5.59KB
  120. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._1.5利用大型数据集.ipynb 212B
  121. llms-from-scratch-cn-main/Translated_Book/ch01/welcome.ipynb 21.17KB
  122. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch01/._welcome.ipynb 212B
  123. llms-from-scratch-cn-main/Translated_Book/ch04/4.7 生成文本.ipynb 6.47KB
  124. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.7 生成文本.ipynb 212B
  125. llms-from-scratch-cn-main/Translated_Book/ch04/4.5 在transfomer模块中连接注意力层和线性层.ipynb 12.37KB
  126. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.5 在transfomer模块中连接注意力层和线性层.ipynb 212B
  127. llms-from-scratch-cn-main/Translated_Book/ch04/.keep
  128. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._.keep 212B
  129. llms-from-scratch-cn-main/Translated_Book/ch04/4.6 编码GPT模型.ipynb 15.42KB
  130. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.6 编码GPT模型.ipynb 212B
  131. llms-from-scratch-cn-main/Translated_Book/ch04/4.1.ipynb 17.16KB
  132. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.1.ipynb 212B
  133. llms-from-scratch-cn-main/Translated_Book/ch04/4.3 实现使用 GELU 激活函数的前馈网络.ipynb 56.12KB
  134. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.3 实现使用 GELU 激活函数的前馈网络.ipynb 212B
  135. llms-from-scratch-cn-main/Translated_Book/ch04/4.2 使用层归一化对激活进行归一化.ipynb 15.38KB
  136. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.2 使用层归一化对激活进行归一化.ipynb 212B
  137. llms-from-scratch-cn-main/Translated_Book/ch04/4.2.ipynb 15.38KB
  138. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.2.ipynb 212B
  139. llms-from-scratch-cn-main/Translated_Book/ch04/4.4 增加快捷链接.ipynb 11.6KB
  140. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.4 增加快捷链接.ipynb 212B
  141. llms-from-scratch-cn-main/Translated_Book/ch04/4.1 从头开始实现 GPT 模型以生成文本.ipynb 17.16KB
  142. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.1 从头开始实现 GPT 模型以生成文本.ipynb 212B
  143. llms-from-scratch-cn-main/Translated_Book/ch04/4.6 编码GPT模型-Copy1.ipynb 15.42KB
  144. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch04/._4.6 编码GPT模型-Copy1.ipynb 212B
  145. llms-from-scratch-cn-main/Translated_Book/ch03/3.1.ipynb 9.1KB
  146. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.1.ipynb 212B
  147. llms-from-scratch-cn-main/Translated_Book/ch03/3.3.ipynb 25.13KB
  148. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.3.ipynb 212B
  149. llms-from-scratch-cn-main/Translated_Book/ch03/3.7.ipynb 2.4KB
  150. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.7.ipynb 212B
  151. llms-from-scratch-cn-main/Translated_Book/ch03/3.5.ipynb 27.44KB
  152. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.5.ipynb 212B
  153. llms-from-scratch-cn-main/Translated_Book/ch03/3.2.ipynb 3.82KB
  154. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.2.ipynb 212B
  155. llms-from-scratch-cn-main/Translated_Book/ch03/3.4.ipynb 25.56KB
  156. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.4.ipynb 212B
  157. llms-from-scratch-cn-main/Translated_Book/ch03/.keep
  158. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._.keep 212B
  159. llms-from-scratch-cn-main/Translated_Book/ch03/3.6.ipynb 23.54KB
  160. __MACOSX/llms-from-scratch-cn-main/Translated_Book/ch03/._3.6.ipynb 212B
  161. llms-from-scratch-cn-main/Translated_Book/img/fig-A-1.jpg 94.35KB
  162. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-A-1.jpg 212B
  163. llms-from-scratch-cn-main/Translated_Book/img/fig-4-12.jpg 138.23KB
  164. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-12.jpg 212B
  165. llms-from-scratch-cn-main/Translated_Book/img/fig-4-13.jpg 167.87KB
  166. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-13.jpg 212B
  167. llms-from-scratch-cn-main/Translated_Book/img/fig-3-26.jpg 115.67KB
  168. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-26.jpg 212B
  169. llms-from-scratch-cn-main/Translated_Book/img/fig-4-9.jpg 129.5KB
  170. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-9.jpg 212B
  171. llms-from-scratch-cn-main/Translated_Book/img/fig-A-2.jpg 131.44KB
  172. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-A-2.jpg 212B
  173. llms-from-scratch-cn-main/Translated_Book/img/fig-3-24.jpg 128.16KB
  174. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-24.jpg 212B
  175. llms-from-scratch-cn-main/Translated_Book/img/fig-3-18.jpg 196.89KB
  176. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-18.jpg 212B
  177. llms-from-scratch-cn-main/Translated_Book/img/fig-4-11.jpg 93.37KB
  178. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-11.jpg 212B
  179. llms-from-scratch-cn-main/Translated_Book/img/fig-3-19.jpg 126.72KB
  180. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-19.jpg 212B
  181. llms-from-scratch-cn-main/Translated_Book/img/fig-4-10.jpg 167.49KB
  182. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-10.jpg 212B
  183. llms-from-scratch-cn-main/Translated_Book/img/fig-3-25.jpg 83.88KB
  184. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-25.jpg 212B
  185. llms-from-scratch-cn-main/Translated_Book/img/fig-A-3.jpg 104.26KB
  186. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-A-3.jpg 212B
  187. llms-from-scratch-cn-main/Translated_Book/img/fig-4-8.jpg 61.73KB
  188. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-8.jpg 212B
  189. llms-from-scratch-cn-main/Translated_Book/img/fig-A-7.jpg 47.44KB
  190. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-A-7.jpg 212B
  191. llms-from-scratch-cn-main/Translated_Book/img/fig-4-14.jpg 87.46KB
  192. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-14.jpg 212B
  193. llms-from-scratch-cn-main/Translated_Book/img/fig-3-21.jpg 43.3KB
  194. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-21.jpg 212B
  195. llms-from-scratch-cn-main/Translated_Book/img/fig-3-20.jpg 45.71KB
  196. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-20.jpg 212B
  197. llms-from-scratch-cn-main/Translated_Book/img/fig-4-15.jpg 168.83KB
  198. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-15.jpg 212B
  199. llms-from-scratch-cn-main/Translated_Book/img/fig-A-6.jpg 70.01KB
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  201. llms-from-scratch-cn-main/Translated_Book/img/fig-A-4.jpg 74.29KB
  202. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-A-4.jpg 212B
  203. llms-from-scratch-cn-main/Translated_Book/img/fig-4-17.jpg 112.79KB
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  205. llms-from-scratch-cn-main/Translated_Book/img/fig-3-22.jpg 172.46KB
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  207. llms-from-scratch-cn-main/Translated_Book/img/fig-2-9.jpg 81.89KB
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  211. llms-from-scratch-cn-main/Translated_Book/img/fig-3-23.jpg 61.8KB
  212. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-3-23.jpg 212B
  213. llms-from-scratch-cn-main/Translated_Book/img/fig-4-16.jpg 155.7KB
  214. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-4-16.jpg 212B
  215. llms-from-scratch-cn-main/Translated_Book/img/fig-A-5.jpg 79.79KB
  216. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-A-5.jpg 212B
  217. llms-from-scratch-cn-main/Translated_Book/img/fig-2-10.jpg 150.51KB
  218. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-2-10.jpg 212B
  219. llms-from-scratch-cn-main/Translated_Book/img/fig-5-9.jpg 213.86KB
  220. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-5-9.jpg 212B
  221. llms-from-scratch-cn-main/Translated_Book/img/fig-5-8.jpg 95.4KB
  222. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-5-8.jpg 212B
  223. llms-from-scratch-cn-main/Translated_Book/img/fig-2-11.jpg 90.18KB
  224. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-2-11.jpg 212B
  225. llms-from-scratch-cn-main/Translated_Book/img/fig-2-13.jpg 107.2KB
  226. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-2-13.jpg 212B
  227. llms-from-scratch-cn-main/Translated_Book/img/fig-2-12.jpg 133.53KB
  228. __MACOSX/llms-from-scratch-cn-main/Translated_Book/img/._fig-2-12.jpg 212B
  229. llms-from-scratch-cn-main/Translated_Book/img/fig-3-9.jpg 66.19KB
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资源介绍:

如果你想从0手写代码，构建大语言模型，本项目很适合你。 本项目 "LLMs From Scratch" 是由 Datawhale 提供的一个从头开始构建类似 ChatGPT 大型语言模型（LLM）的实践教程。 我们旨在通过详细的指导、代码示例和深度学习资源，帮助开发者和研究者掌握创建大语言模型和大语言模型架构的核心技术。 本项目包括了从0逐步构建GLM4\Llama3\RWKV6的教程，从0构建大模型，一起深入理解大模型原理。
# 从头开始实现llama3 在这个文件中，我逐个张量和矩阵地从头实现了llama3。 本地可以运行：llama3-from-scratch.ipynb
此外，我将直接从meta提供给llama3的模型文件中加载张量，你需要在运行此文件之前下载权重。 这是下载权重的官方链接: [点击这里下载权重](https://llama.meta.com/llama-downloads/)
https://hf-mirror.com/NousResearch/Meta-Llama-3-8B https://gitee.com/hf-models/Meta-Llama-3-8B-Instruct/ ## 分词器 我不打算实现一个BPE分词器（但是Andrej Karpathy有一个非常干净的实现）。
他的实现链接: [点击这里查看他的实现](https://github.com/karpathy/minbpe)
```python %env HF_ENDPOINT = "https://hf-mirror.com" ``` env: HF_ENDPOINT="https://hf-mirror.com" ```python %pip install blobfile -q ``` Note: you may need to restart the kernel to use updated packages. ```python from pathlib import Path import tiktoken from tiktoken.load import load_tiktoken_bpe import torch import json import matplotlib.pyplot as plt tokenizer_path = "./tokenizer.model" special_tokens = [ "<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|reserved_special_token_2|>", "<|reserved_special_token_3|>", "<|start_header_id|>", "<|end_header_id|>", "<|reserved_special_token_4|>", "<|eot_id|>", # end of turn ] + [f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5)] mergeable_ranks = load_tiktoken_bpe(tokenizer_path) tokenizer = tiktoken.Encoding( name=Path(tokenizer_path).name, pat_str=r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+", mergeable_ranks=mergeable_ranks, special_tokens={token: len(mergeable_ranks) + i for i, token in enumerate(special_tokens)}, ) tokenizer.decode(tokenizer.encode("hello world!")) ``` 'hello world!' ## 读取模型文件 通常，读取模型文件取决于模型类的编写方式以及其中的变量名。
但由于我们是从头开始实现llama3，我们将逐个张量地读取文件。
可以在这里下载模型：https://gitee.com/hf-models/Meta-Llama-3-8B-Instruct/blob/main/original/consolidated.00.pth ```python !wget 'https://lfs.gitee.com/api/lfs/storage/projects/34266234/be52262c9289304f3e8240e0749bf257bc04264405a86cd4de38efb9068724ee?Expires=1716626632&Signature=xgDOu9JHNM6ECazR3nA4NQHwXs%2BiG%2BCtnzza6ekSuqs%3D&FileName=consolidated.00.pth' ``` --2024-05-25 16:24:15-- https://lfs.gitee.com/api/lfs/storage/projects/34266234/be52262c9289304f3e8240e0749bf257bc04264405a86cd4de38efb9068724ee?Expires=1716626632&Signature=xgDOu9JHNM6ECazR3nA4NQHwXs%2BiG%2BCtnzza6ekSuqs%3D&FileName=consolidated.00.pth Resolving lfs.gitee.com (lfs.gitee.com)... 180.76.198.180 Connecting to lfs.gitee.com (lfs.gitee.com)|180.76.198.180|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 16060617592 (15G) [application/octet-stream] Saving to: ‘be52262c9289304f3e8240e0749bf257bc04264405a86cd4de38efb9068724ee?Expires=1716626632&Signature=xgDOu9JHNM6ECazR3nA4NQHwXs+iG+Ctnzza6ekSuqs=&FileName=consolidated.00.pth’ 0% [ ] 105,193,134 453KB/s eta 11h 21m^C 我的机器12s可以载入，接下来仅用cpu进行推理，我这边内存30G足够了，然后cpu推理一个词大约30s，稍微慢了一些，不过我们主要理解原理 ```python model = torch.load("/data1/ckw/consolidated.00.pth") print(json.dumps(list(model.keys())[:20], indent=4)) ``` [ "tok_embeddings.weight", "layers.0.attention.wq.weight", "layers.0.attention.wk.weight", "layers.0.attention.wv.weight", "layers.0.attention.wo.weight", "layers.0.feed_forward.w1.weight", "layers.0.feed_forward.w3.weight", "layers.0.feed_forward.w2.weight", "layers.0.attention_norm.weight", "layers.0.ffn_norm.weight", "layers.1.attention.wq.weight", "layers.1.attention.wk.weight", "layers.1.attention.wv.weight", "layers.1.attention.wo.weight", "layers.1.feed_forward.w1.weight", "layers.1.feed_forward.w3.weight", "layers.1.feed_forward.w2.weight", "layers.1.attention_norm.weight", "layers.1.ffn_norm.weight", "layers.2.attention.wq.weight" ] ```python with open("./params.json", "r") as f: config = json.load(f) config ``` {'dim': 4096, 'n_layers': 32, 'n_heads': 32, 'n_kv_heads': 8, 'vocab_size': 128256, 'multiple_of': 1024, 'ffn_dim_multiplier': 1.3, 'norm_eps': 1e-05, 'rope_theta': 500000.0} ## 我们使用这个配置来推断模型的细节，比如： 1. 模型有32个Transformer层 2. 每个多头注意力块有32个头 3. 词汇表大小，等等 ```python dim = config["dim"] n_layers = config["n_layers"] n_heads = config["n_heads"] n_kv_heads = config["n_kv_heads"] vocab_size = config["vocab_size"] multiple_of = config["multiple_of"] ffn_dim_multiplier = config["ffn_dim_multiplier"] norm_eps = config["norm_eps"] rope_theta = torch.tensor(config["rope_theta"]) ``` ## 将文本转换为标记 这里我们使用tiktoken（我认为是OpenAI的一个库）作为分词器
```python prompt = "the answer to the ultimate question of life, the universe, and everything is " tokens = [128000] + tokenizer.encode(prompt) print(tokens) tokens = torch.tensor(tokens) prompt_split_as_tokens = [tokenizer.decode([token.item()]) for token in tokens] print(prompt_split_as_tokens) ``` [128000, 1820, 4320, 311, 279, 17139, 3488, 315, 2324, 11, 279, 15861, 11, 323, 4395, 374, 220] ['<|begin_of_text|>', 'the', ' answer', ' to', ' the', ' ultimate', ' question', ' of', ' life', ',', ' the', ' universe', ',', ' and', ' everything', ' is', ' '] ## 将标记转换为它们的嵌入向量 这是代码库中我唯一使用内置神经网络模块的部分。
无论如何，我们的[17x1]标记现在是[17x4096]，即长度为4096的17个嵌入向量（每个标记一个）。

注意: 跟踪形状，这样可以更容易理解所有内容
```python embedding_layer = torch.nn.Embedding(vocab_size, dim) embedding_layer.weight.data.copy_(model["tok_embeddings.weight"]) token_embeddings_unnormalized = embedding_layer(tokens).to(torch.bfloat16) token_embeddings_unnormalized.shape ``` torch.Size([17, 4096]) ## 然后我们使用RMS归一化来标准化嵌入向量 请注意，在此步骤之后，形状不会改变，只是值被标准化了。
需要记住的一些事情，我们需要一个norm_eps（来自配置），因为我们不希望意外地将RMS设置为0并除以0。
以下是公式：
```python # def rms_norm(tensor, norm_weights): # rms = (tensor.pow(2).mean(-1, keepdim=True) + norm_eps)**0.5 # return tensor * (norm_weights / rms) def rms_norm(tensor, norm_weights): return (tensor * torch.rsqrt(tensor.pow(2).mean(-1, keepdim=True) + norm_eps)) * norm_weights ``` # 构建Transformer的第一层 ### 标准化 你会看到我从模型字典中访问layer.0（这是第一层）。
无论如何，所以在我们标准化后，形状仍然是[17x4096]，与嵌入向量相同，但是标准化了
```python token_embeddings = rms_norm(token_embeddings_unnormalized, model["layers.0.attention_norm.weight"]) to
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