Add Pre Commit checks

.github/workflows/ci-checks.yaml

@@ -0,0 +1,33 @@
+name: CI Checks
+
+on:
+  push:
+    branches:
+      - main
+
+  pull_request:
+    branches:
+      - "*" # Run on all branches
+
+env:
+  MIN_PYTHON_VERSION: 3.11
+
+jobs:
+  job-pre-commit-check:
+    name: Pre-Commit Check
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ env.MIN_PYTHON_VERSION }}
+
+      - name: Install dependencies
+        run: pip install -r requirements-dev.txt
+
+      - name: Run pre-commit
+        run: pre-commit run --all-files

adi_function_app/README.md

@@ -42,7 +42,7 @@ Using the [Phi-3 Technical Report: A Highly Capable Language Model Locally on Yo
 
 ```json
 {
-    "content": "\n<table>\n<caption>Table 1: Comparison results on RepoQA benchmark.</caption>\n<tr>\n<th>Model</th>\n<th>Ctx Size</th>\n<th>Python</th>\n<th>C++</th>\n<th>Rust</th>\n<th>Java</th>\n<th>TypeScript</th>\n<th>Average</th>\n</tr>\n<tr>\n<td>gpt-4O-2024-05-13</td>\n<td>128k</td>\n<td>95</td>\n<td>80</td>\n<td>85</td>\n<td>96</td>\n<td>97</td>\n<td>90.6</td>\n</tr>\n<tr>\n<td>gemini-1.5-flash-latest</td>\n<td>1000k</td>\n<td>93</td>\n<td>79</td>\n<td>87</td>\n<td>94</td>\n<td>97</td>\n<td>90</td>\n</tr>\n<tr>\n<td>Phi-3.5-MoE</td>\n<td>128k</td>\n<td>89</td>\n<td>74</td>\n<td>81</td>\n<td>88</td>\n<td>95</td>\n<td>85</td>\n</tr>\n<tr>\n<td>Phi-3.5-Mini</td>\n<td>128k</td>\n<td>86</td>\n<td>67</td>\n<td>73</td>\n<td>77</td>\n<td>82</td>\n<td>77</td>\n</tr>\n<tr>\n<td>Llama-3.1-8B-Instruct</td>\n<td>128k</td>\n<td>80</td>\n<td>65</td>\n<td>73</td>\n<td>76</td>\n<td>63</td>\n<td>71</td>\n</tr>\n<tr>\n<td>Mixtral-8x7B-Instruct-v0.1</td>\n<td>32k</td>\n<td>66</td>\n<td>65</td>\n<td>64</td>\n<td>71</td>\n<td>74</td>\n<td>68</td>\n</tr>\n<tr>\n<td>Mixtral-8x22B-Instruct-v0.1</td>\n<td>64k</td>\n<td>60</td>\n<td>67</td>\n<td>74</td>\n<td>83</td>\n<td>55</td>\n<td>67.8</td>\n</tr>\n</table>\n\n\nsuch as Arabic, Chinese, Russian, Ukrainian, and Vietnamese, with average MMLU-multilingual scores\nof 55.4 and 47.3, respectively. Due to its larger model capacity, phi-3.5-MoE achieves a significantly\nhigher average score of 69.9, outperforming phi-3.5-mini.\n\nMMLU(5-shot) MultiLingual\n\nPhi-3-mini\n\nPhi-3.5-mini\n\nPhi-3.5-MoE\n\n\n<!-- FigureContent=\"**Technical Analysis of Figure 4: Comparison of phi-3-mini, phi-3.5-mini and phi-3.5-MoE on MMLU-Multilingual tasks**\n\n1. **Overview:**\n   - The image is a bar chart comparing the performance of three different models—phi-3-mini, phi-3.5-mini, and phi-3.5-MoE—on MMLU-Multilingual tasks across various languages.\n\n2. **Axes:**\n   - The x-axis represents the languages in which the tasks were performed. The languages listed are: Arabic, Chinese, Dutch, French, German, Italian, Russian, Spanish, Ukrainian, Vietnamese, and English.\n   - The y-axis represents the performance, likely measured in percentage or score, ranging from 0 to 90.\n\n3. **Legend:**\n   - The chart uses three different colors to represent the three models:\n     - Orange bars represent the phi-3-mini model.\n     - Green bars represent the phi-3.5-mini model.\n     - Blue bars represent the phi-3.5-MoE model.\n\n4. **Data Interpretation:**\n   - Across all languages, the phi-3.5-MoE (blue bars) consistently outperforms the other two models, showing the highest bars.\n   - The phi-3.5-mini (green bars) shows better performance than the phi-3-mini (orange bars) in most languages, but not at the level of phi-3.5-MoE.\n\n5. **Language-specific Insights:**\n   - **Arabic**: phi-3.5-MoE shows significantly higher performance compared to the other two models, with phi-3.5-mini outperforming phi-3-mini.\n   - **Chinese**: A similar trend is observed as in Arabic, with phi-3.5-MoE leading by a wide margin.\n   - **Dutch**: Performance is roughly similar between phi-3.5-mini and phi-3.5-MoE, with phi-3.5-MoE being slightly better.\n   - **French**: A clear distinction in performance, with phi-3.5-MoE far exceeding the other two.\n   - **German**: phi-3.5-MoE leads, followed by phi-3.5-mini, while phi-3-mini lags significantly behind.\n   - **Italian**: The performance gap narrows between phi-3.5-mini and phi-3.5-MoE, but the latter is still superior.\n   - **Russian**: phi-3.5-MoE shows noticeably higher performance.\n   - **Spanish**: The performance trend is consistent with the previous languages, with phi-3.5-MoE leading.\n   - **Ukrainian**: A substantial lead by phi-3.5-MoE.\n   - **Vietnamese**: An anomaly where all models show closer performance, yet phi-3.5-MoE still leads.\n   - **English**: The highest performance is seen in English, with phi-3.5-MoE nearly reaching the maximum score.\n\n6. **Conclusion:**\n   - The phi-3.5-MoE model consistently outperforms the phi-3-mini and phi-3.5-mini models across all MMLU-Multilingual tasks.\n   - The phi-3.5-mini model shows a general improvement over the phi-3-mini, but the improvement is not as significant as phi-3.5-MoE.\n\nThis structured analysis provides a comprehensive understanding of the comparative performance of the mentioned models across multilingual tasks.\" -->\n\n\nWe evaluate the phi-3.5-mini and phi-3.5-MoE models on two long-context understanding tasks:\nRULER [HSK+24] and RepoQA [LTD+24]. As shown in Tables 1 and 2, both phi-3.5-MoE and phi-\n3.5-mini outperform other open-source models with larger sizes, such as Llama-3.1-8B, Mixtral-8x7B,\nand Mixtral-8x22B, on the RepoQA task, and achieve comparable performance to Llama-3.1-8B on\nthe RULER task. However, we observe a significant performance drop when testing the 128K context\nwindow on the RULER task. We suspect this is due to the lack of high-quality long-context data in\nmid-training, an issue we plan to address in the next version of the model release.\n\nIn the table 3, we present a detailed evaluation of the phi-3.5-mini and phi-3.5-MoE models\ncompared with recent SoTA pretrained language models, such as GPT-4o-mini, Gemini-1.5 Flash, and\nopen-source models like Llama-3.1-8B and the Mistral models. The results show that phi-3.5-mini\nachieves performance comparable to much larger models like Mistral-Nemo-12B and Llama-3.1-8B, while\nphi-3.5-MoE significantly outperforms other open-source models, offers performance comparable to\nGemini-1.5 Flash, and achieves above 90% of the average performance of GPT-4o-mini across various\nlanguage benchmarks.\n\n\n\n\n",
+    "content": "\n<table>\n<caption>Table 1: Comparison results on RepoQA benchmark.</caption>\n<tr>\n<th>Model</th>\n<th>Ctx Size</th>\n<th>Python</th>\n<th>C++</th>\n<th>Rust</th>\n<th>Java</th>\n<th>TypeScript</th>\n<th>Average</th>\n</tr>\n<tr>\n<td>gpt-4O-2024-05-13</td>\n<td>128k</td>\n<td>95</td>\n<td>80</td>\n<td>85</td>\n<td>96</td>\n<td>97</td>\n<td>90.6</td>\n</tr>\n<tr>\n<td>gemini-1.5-flash-latest</td>\n<td>1000k</td>\n<td>93</td>\n<td>79</td>\n<td>87</td>\n<td>94</td>\n<td>97</td>\n<td>90</td>\n</tr>\n<tr>\n<td>Phi-3.5-MoE</td>\n<td>128k</td>\n<td>89</td>\n<td>74</td>\n<td>81</td>\n<td>88</td>\n<td>95</td>\n<td>85</td>\n</tr>\n<tr>\n<td>Phi-3.5-Mini</td>\n<td>128k</td>\n<td>86</td>\n<td>67</td>\n<td>73</td>\n<td>77</td>\n<td>82</td>\n<td>77</td>\n</tr>\n<tr>\n<td>Llama-3.1-8B-Instruct</td>\n<td>128k</td>\n<td>80</td>\n<td>65</td>\n<td>73</td>\n<td>76</td>\n<td>63</td>\n<td>71</td>\n</tr>\n<tr>\n<td>Mixtral-8x7B-Instruct-v0.1</td>\n<td>32k</td>\n<td>66</td>\n<td>65</td>\n<td>64</td>\n<td>71</td>\n<td>74</td>\n<td>68</td>\n</tr>\n<tr>\n<td>Mixtral-8x22B-Instruct-v0.1</td>\n<td>64k</td>\n<td>60</td>\n<td>67</td>\n<td>74</td>\n<td>83</td>\n<td>55</td>\n<td>67.8</td>\n</tr>\n</table>\n\n\nsuch as Arabic, Chinese, Russian, Ukrainian, and Vietnamese, with average MMLU-multilingual scores\nof 55.4 and 47.3, respectively. Due to its larger model capacity, phi-3.5-MoE achieves a significantly\nhigher average score of 69.9, outperforming phi-3.5-mini.\n\nMMLU(5-shot) MultiLingual\n\nPhi-3-mini\n\nPhi-3.5-mini\n\nPhi-3.5-MoE\n\n\n<!-- FigureContent=\"**Technical Analysis of Figure 4: Comparison of phi-3-mini, phi-3.5-mini and phi-3.5-MoE on MMLU-Multilingual tasks**\n\n1. **Overview:**\n   - The image is a bar chart comparing the performance of three different models—phi-3-mini, phi-3.5-mini, and phi-3.5-MoE—on MMLU-Multilingual tasks across various languages.\n\n2. **Axes:**\n   - The x-axis represents the languages in which the tasks were performed. The languages listed are: Arabic, Chinese, Dutch, French, German, Italian, Russian, Spanish, Ukrainian, Vietnamese, and English.\n   - The y-axis represents the performance, likely measured in percentage or score, ranging from 0 to 90.\n\n3. **Legend:**\n   - The chart uses three different colors to represent the three models:\n     - Orange bars represent the phi-3-mini model.\n     - Green bars represent the phi-3.5-mini model.\n     - Blue bars represent the phi-3.5-MoE model.\n\n4. **Data Interpretation:**\n   - Across all languages, the phi-3.5-MoE (blue bars) consistently outperforms the other two models, showing the highest bars.\n   - The phi-3.5-mini (green bars) shows better performance than the phi-3-mini (orange bars) in most languages, but not at the level of phi-3.5-MoE.\n\n5. **Language-specific Insights:**\n   - **Arabic**: phi-3.5-MoE shows significantly higher performance compared to the other two models, with phi-3.5-mini outperforming phi-3-mini.\n   - **Chinese**: A similar trend is observed as in Arabic, with phi-3.5-MoE leading by a wide margin.\n   - **Dutch**: Performance is roughly similar between phi-3.5-mini and phi-3.5-MoE, with phi-3.5-MoE being slightly better.\n   - **French**: A clear distinction in performance, with phi-3.5-MoE far exceeding the other two.\n   - **German**: phi-3.5-MoE leads, followed by phi-3.5-mini, while phi-3-mini lags significantly behind.\n   - **Italian**: The performance gap narrows between phi-3.5-mini and phi-3.5-MoE, but the latter is still superior.\n   - **Russian**: phi-3.5-MoE shows noticeably higher performance.\n   - **Spanish**: The performance trend is consistent with the previous languages, with phi-3.5-MoE leading.\n   - **Ukrainian**: A substantial lead by phi-3.5-MoE.\n   - **Vietnamese**: An anomaly where all models show closer performance, yet phi-3.5-MoE still leads.\n   - **English**: The highest performance is seen in English, with phi-3.5-MoE nearly reaching the maximum score.\n\n6. **Conclusion:**\n   - The phi-3.5-MoE model consistently outperforms the phi-3-mini and phi-3.5-mini models across all MMLU-Multilingual tasks.\n   - The phi-3.5-mini model shows a general improvement over the phi-3-mini, but the improvement is not as significant as phi-3.5-MoE.\n\nThis structured analysis provides a comprehensive understanding of the comparative performance of the mentioned models across multilingual tasks.\" -->\n\n\n We evaluate the phi-3.5-mini and phi-3.5-MoE models on two long-context understanding tasks:\nRULER [HSK+24] and RepoQA [LTD+24]. As shown in Tables 1 and 2, both phi-3.5-MoE and phi-\n3.5-mini outperform other open-source models with larger sizes, such as Llama-3.1-8B, Mixtral-8x7B,\nand Mixtral-8x22B, on the RepoQA task, and achieve comparable performance to Llama-3.1-8B on\nthe RULER task. However, we observe a significant performance drop when testing the 128K context\nwindow on the RULER task. We suspect this is due to the lack of high-quality long-context data in\nmid-training, an issue we plan to address in the next version of the model release.\n\n In the table 3, we present a detailed evaluation of the phi-3.5-mini and phi-3.5-MoE models\ncompared with recent SoTA pretrained language models, such as GPT-4o-mini, Gemini-1.5 Flash, and\nopen-source models like Llama-3.1-8B and the Mistral models. The results show that phi-3.5-mini\nachieves performance comparable to much larger models like Mistral-Nemo-12B and Llama-3.1-8B, while\nphi-3.5-MoE significantly outperforms other open-source models, offers performance comparable to\nGemini-1.5 Flash, and achieves above 90% of the average performance of GPT-4o-mini across various\nlanguage benchmarks.\n\n\n\n\n",
     "sections": [],
     "page_number": 7
 }