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--- |
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language: |
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- en |
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license: cc-by-nc-nd-4.0 |
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tags: |
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- code |
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datasets: |
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- ajibawa-2023/Code-290k-ShareGPT |
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model-index: |
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- name: Code-290k-13B |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 56.06 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 81.55 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 51.99 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 37.65 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 72.69 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 17.82 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-13B |
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name: Open LLM Leaderboard |
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--- |
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**Code-290k-13B** |
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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. |
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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. |
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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) . |
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This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation. |
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I have released the new data [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) on which this Model is trained. |
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**Training:** |
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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. |
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This is a full fine tuned model. Links for quantized models are given below. |
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**GPTQ, GGUF, AWQ & Exllama** |
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GPTQ: [Link](https://huggingface.co/TheBloke/Code-290k-13B-GPTQ) |
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GGUF: [Link](https://huggingface.co/TheBloke/Code-290k-13B-GGUF) |
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AWQ: [Link](https://huggingface.co/TheBloke/Code-290k-13B-AWQ) |
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Exllama v2: [Link](https://huggingface.co/bartowski/Code-290k-13B-exl2) |
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Extremely thankful to [TheBloke](https://huggingface.co/TheBloke) and [Bartowski](https://huggingface.co/bartowski) for making Quantized versions of the model. |
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**Example Prompt:** |
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``` |
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This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation. |
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Context |
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You are a helpful AI assistant. |
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USER: <prompt> |
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ASSISTANT: |
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``` |
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You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 . |
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I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development. |
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Thank you for your love & support. |
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**Example Output** |
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Will update soon. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-290k-13B) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |52.96| |
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|AI2 Reasoning Challenge (25-Shot)|56.06| |
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|HellaSwag (10-Shot) |81.55| |
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|MMLU (5-Shot) |51.99| |
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|TruthfulQA (0-shot) |37.65| |
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|Winogrande (5-shot) |72.69| |
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|GSM8k (5-shot) |17.82| |
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