190 lines
5.6 KiB
Markdown
190 lines
5.6 KiB
Markdown
---
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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-74k-ShareGPT
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model-index:
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- name: Code-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: 57.34
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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-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: 83.28
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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-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: 53.17
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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-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: 42.46
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-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: 73.56
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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-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: 19.03
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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-13B
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name: Open LLM Leaderboard
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---
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**Code-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 74000 set of codes. Each set having 2 conversations.
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Along with Python, Java, JavaScript, GO, C++, Rust etc. code with detailed explanation is used for training purpose. It is built upon using my existing Dataset [Python-Code-23k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-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-74k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-74k-ShareGPT) on which this Model is trained.
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**Training:**
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Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 42 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**
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GPTQ: [Link](https://huggingface.co/TheBloke/Code-13B-GPTQ)
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GGUF: [Link](https://huggingface.co/TheBloke/Code-13B-GGUF)
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AWQ: [Link](https://huggingface.co/TheBloke/Code-13B-AWQ)
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Extremely thankful to [TheBloke](https://huggingface.co/TheBloke) for making Quantized versions of 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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1. Navier-Stokes Equation Solver
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2. KSC Complexity
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3. GO
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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-13B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |54.81|
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|AI2 Reasoning Challenge (25-Shot)|57.34|
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|HellaSwag (10-Shot) |83.28|
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|MMLU (5-Shot) |53.17|
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|TruthfulQA (0-shot) |42.46|
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|Winogrande (5-shot) |73.56|
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|GSM8k (5-shot) |19.03|
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