初始化项目，由ModelHub XC社区提供模型

Model: InferenceIllusionist/Excalibur-7b-DPO
Source: Original Platform

README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+- finetune
+- dpo
+- chatml
+base_model:
+- InferenceIllusionist/Excalibur-7b
+datasets:
+- Intel/orca_dpo_pairs
+model-index:
+- name: Excalibur-7b-DPO
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 70.9
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 87.93
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 65.46
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 70.82
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 82.48
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 65.43
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO
+      name: Open LLM Leaderboard
+---
+
+
+# Excalibur-7b-DPO
+
+<img src="https://i.imgur.com/pbPbqq0.jpeg" width="550"/>
+
+An initial foray into the world of fine-tuning. The goal of this release was to amplify the quality of the original model's responses, in particular for vision use cases*
+
+<b>Weighted (Importance Matrix) Quants available [here](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-iMat-GGUF)</b>
+
+<b>Static (Legacy) quants available [here](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-GGUF)</b>
+
+
+## Notes & Methodology
+* [Excalibur-7b](https://huggingface.co/InferenceIllusionist/Excalibur-7b) fine-tuned with Direct Preference Optimization (DPO) using Intel/orca_dpo_pairs
+* This is a quick experiment to determine the impact of DPO finetuning on the Excelsior-7b base model
+* Ran for a little over an hour on a single A100
+* Fine-tuning succeeded in making model conversational and more well-rounded
+* Benchmark scores increased in the following categories versus base Excelsior-7b:
+  * ARC: 69.71 -> <b>70.9</b>
+  * HellaSwag: 87.56 -> <b>87.93</b>
+  * TruthfulQA: 67.24 -> <b>70.82</b>
+  * Average: 73.6 -> <b>73.84</b>
+* Precision: bfloat16
+
+
+## Sample Question - Vision
+<img src="https://i.imgur.com/7aRWtzU.jpeg" width="425"/>
+
+*<b>Requires additional mmproj file. You have two options for vision functionality (available inside this repo):</b>
+ * [Quantized - Limited VRAM Option (197mb)](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-GGUF/resolve/main/mistral-7b-mmproj-v1.5-Q4_1.gguf?download=true)
+ * [Unquantized - Premium Option / Best Quality (596mb)](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-GGUF/resolve/main/mmproj-model-f16.gguf?download=true)
+
+Select the gguf file of your choice in [Koboldcpp](https://github.com/LostRuins/koboldcpp/releases/) as usual, then make sure to choose the mmproj file above in the LLaVA mmproj field of the model submenu:
+<img src="https://i.imgur.com/x8vqH29.png" width="425"/>
+
+## Prompt Format
+* For best results please use ChatML for the prompt format. Alpaca may also work.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_InferenceIllusionist__Excalibur-7b-DPO)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |73.84|
+|AI2 Reasoning Challenge (25-Shot)|70.90|
+|HellaSwag (10-Shot)              |87.93|
+|MMLU (5-Shot)                    |65.46|
+|TruthfulQA (0-shot)              |70.82|
+|Winogrande (5-shot)              |82.48|
+|GSM8k (5-shot)                   |65.43|
+