---
library_name: transformers
license: apache-2.0
datasets:
- nickrosh/Evol-Instruct-Code-80k-v1
metrics:
- accuracy
pipeline_tag: text-generation
base_model: AIDC-ai-business/Luban-13B
---

<!-- 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!


## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 512

### Framework versions

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

# [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.                  | 17.2   |
| ARC (25-shot)         | 22.7          |
| HellaSwag (10-shot)   | 25.04    |
| MMLU (5-shot)         | 23.12         |
| TruthfulQA (0-shot)   | 0.0   |
| Winogrande (5-shot)   | 49.57   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 0.0         |