README.md

---
license: apache-2.0
library_name: transformers
base_model: AIDC-ai-business/Luban-13B
datasets:
- nickrosh/Evol-Instruct-Code-80k-v1
metrics:
- accuracy
pipeline_tag: text-generation
model-index:
- name: panda-coder-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: 22.7
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aiplanet/panda-coder-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: 25.04
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aiplanet/panda-coder-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: 23.12
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aiplanet/panda-coder-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: 0.0
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aiplanet/panda-coder-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: 49.57
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aiplanet/panda-coder-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: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aiplanet/panda-coder-13B
      name: Open LLM Leaderboard
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Panda-Coder 🐼

![pandacoder](https://media.licdn.com/dms/image/D5622AQEHi1BVUBnUUA/feedshare-shrink_800/0/1697200946153?e=1700092800&v=beta&t=RPv3bcR22-yHa48Y-W44-1xs30asSShFeD0aqo2TOvI)

Panda Coder is a state-of-the-art LLM capable of generating code on the NLP based Instructions

## Model description

🤖 Model Description: Panda-Coder is a state-of-the-art LLM, a fine-tuned model, specifically designed to generate code based on natural language instructions. It's the result of relentless innovation and meticulous fine-tuning, all to make coding easier and more accessible for everyone.

🔗 Key Features:

🌟 NLP-Based Coding: With Panda-Coder, you can transform your plain text instructions into functional code effortlessly. No need to grapple with syntax and semantics - it understands your language.

🎯 Precision and Efficiency: The model is tailored for accuracy, ensuring your code is not just functional but also efficient.

✨ Unleash Creativity: Whether you're a novice or an expert coder, Panda-Coder is here to support your coding journey, offering creative solutions to your programming challenges.

📚 Evol Instruct Code: It's built on the robust Evol Instruct Code 80k-v1 dataset, guaranteeing top-notch code generation.

📢 What's Next?: We believe in continuous improvement and are excited to announce that in our next release, Panda-Coder will be enhanced with a custom dataset. This dataset will not only expand the language support but also include hardware programming languages like MATLAB, Embedded C, and Verilog. 🧰💡

## Get in Touch

You can schedule 1:1 meeting with our DevRel & Community Team to get started with AI Planet Open Source LLMs and GenAI Stack. Schedule the call here: [https://calendly.com/jaintarun](https://calendly.com/jaintarun)

Stay tuned for more updates and be a part of the coding evolution. Join us on this exciting journey as we make AI accessible to all at AI Planet!


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

### Citation

```

@misc {lucifertrj,
	author       = { {Tarun Jain} },
	title        = { Panda Coder-13B by AI Planet},
	year         = 2023,
	url          = { https://huggingface.co/aiplanet/panda-coder-13B },
	publisher    = { Hugging Face }
}

```
# [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_aiplanet__panda-coder-13B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |20.07|
|AI2 Reasoning Challenge (25-Shot)|22.70|
|HellaSwag (10-Shot)              |25.04|
|MMLU (5-Shot)                    |23.12|
|TruthfulQA (0-shot)              | 0.00|
|Winogrande (5-shot)              |49.57|
|GSM8k (5-shot)                   | 0.00|