初始化项目，由ModelHub XC社区提供模型

Model: ajibawa-2023/Code-290k-13B
Source: Original Platform

README.md

@@ -0,0 +1,178 @@
+---
+language:
+- en
+license: cc-by-nc-nd-4.0
+tags:
+- code
+datasets:
+- ajibawa-2023/Code-290k-ShareGPT
+model-index:
+- name: Code-290k-13B
+  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: 56.06
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B
+      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: 81.55
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B
+      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: 51.99
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B
+      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: 37.65
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B
+      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: 72.69
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B
+      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: 17.82
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B
+      name: Open LLM Leaderboard
+---
+
+
+**Code-290k-13B**
+
+Large Language Models (LLMs) are good with code generations. Sometimes they do make mistakes in code generation. How about if they can give detailed explanation along with the code.
+This is what I have tried over here. The base Llama-2 model was used for training purpose. It is trained on around **290000** set of codes. Each set having 2 conversations.
+Along with Python, Java, JavaScript, GO, C++, Rust, Ruby, Sql, MySql, R, Julia, Haskell, etc. code with detailed explanation is used for training purpose. It is built upon using my existing Datasets [Python-Code-23k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT) and [Code-74k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-74k-ShareGPT) .
+This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation. 
+
+I have released the new data [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) on which this Model is trained.
+
+**Training:**
+
+Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 165 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.
+
+
+This is a full fine tuned model. Links for quantized models are given below.
+
+
+**GPTQ, GGUF, AWQ & Exllama**
+
+GPTQ: [Link](https://huggingface.co/TheBloke/Code-290k-13B-GPTQ)
+
+GGUF: [Link](https://huggingface.co/TheBloke/Code-290k-13B-GGUF)
+
+AWQ: [Link](https://huggingface.co/TheBloke/Code-290k-13B-AWQ)
+
+Exllama v2: [Link](https://huggingface.co/bartowski/Code-290k-13B-exl2)
+
+
+Extremely thankful to [TheBloke](https://huggingface.co/TheBloke) and [Bartowski](https://huggingface.co/bartowski) for making Quantized versions of the model.
+
+
+**Example Prompt:**
+```
+This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation.
+
+Context
+You are a helpful AI assistant.
+
+USER: <prompt>
+ASSISTANT:
+```
+
+You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .
+
+I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
+
+Thank you for your love & support.
+
+**Example Output**
+
+Will update soon.
+# [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_ajibawa-2023__Code-290k-13B)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |52.96|
+|AI2 Reasoning Challenge (25-Shot)|56.06|
+|HellaSwag (10-Shot)              |81.55|
+|MMLU (5-Shot)                    |51.99|
+|TruthfulQA (0-shot)              |37.65|
+|Winogrande (5-shot)              |72.69|
+|GSM8k (5-shot)                   |17.82|
+