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---
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 🐼
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 |
|