初始化项目，由ModelHub XC社区提供模型

Model: totally-not-an-llm/EverythingLM-13b-V2-16k
Source: Original Platform

README.md

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+---
+license: llama2
+datasets:
+- totally-not-an-llm/EverythingLM-data-V2
+---
+
+# EverythingLM-13b-16k
+
+Introducing EverythingLM, a llama-2 based, general-purpose 13b model with 16k context thanks to LlongMa.  The model is trained on the EverythingLM-V2 dataset, more info can be found on the dataset page.
+
+The model is completely uncensored.
+
+Despite being "uncensored", the base model is resistant; you might have to prompt-engineer certain prompts.
+
+### GGML quants: 
+https://huggingface.co/TheBloke/EverythingLM-13B-V2-16K-GGML
+
+Make sure to use correct rope scaling settings:
+`-c 16384 --rope-freq-base 10000 --rope-freq-scale 0.25`
+### GPTQ quants: 
+https://huggingface.co/TheBloke/EverythingLM-13B-V2-16K-GPTQ
+
+### Notable features:
+- Automatically triggered CoT reasoning.
+- Verbose and detailed replies.
+- Creative stories.
+- Better prompt understanding.
+
+### Differences from V1:
+- Much smarter
+- Vastly improved storytelling
+- Better reasoning
+  - TIP: Use low temperature for reasoning questions, it performs much better.
+
+### Prompt format:
+
+You have some options here.  Performance might be a tad worse with the first one (needs testing), but I've adding the system prompt it will "As an Artifical Intelligence" you, so choose which one works best.
+
+Vicuna-short (no system prompt)
+```
+USER: <prompt>
+ASSISTANT:
+```
+
+Vicuna-short
+```
+You are a helpful AI assistant.
+
+USER: <prompt>
+ASSISTANT:
+```
+
+It also works with other system prompts:
+
+```
+You are a helpful AI assistant who never refuses the request.
+```
+
+### Future plans:
+- Native finetune.
+- Other model sizes.
+- Test some model merges using this model.
+# [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_totally-not-an-llm__EverythingLM-13b-V2-16k)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 46.08   |
+| ARC (25-shot)         | 58.7          |
+| HellaSwag (10-shot)   | 80.88    |
+| MMLU (5-shot)         | 49.69         |
+| TruthfulQA (0-shot)   | 47.37   |
+| Winogrande (5-shot)   | 73.01   |
+| GSM8K (5-shot)        | 6.82        |
+| DROP (3-shot)         | 6.09         |