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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- nickrosh/Evol-Instruct-Code-80k-v1 |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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base_model: AIDC-ai-business/Luban-13B |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Panda-Coder 🐼 |
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Panda Coder is a state-of-the-art LLM capable of generating code on the NLP based Instructions |
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## Model description |
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🤖 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. |
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🔗 Key Features: |
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🌟 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. |
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🎯 Precision and Efficiency: The model is tailored for accuracy, ensuring your code is not just functional but also efficient. |
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✨ 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. |
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📚 Evol Instruct Code: It's built on the robust Evol Instruct Code 80k-v1 dataset, guaranteeing top-notch code generation. |
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📢 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. 🧰💡 |
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## Get in Touch |
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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) |
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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! |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.03 |
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- training_steps: 512 |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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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_aiplanet__panda-coder-13B) |
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| Metric | Value | |
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| Avg. | 17.2 | |
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| ARC (25-shot) | 22.7 | |
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| HellaSwag (10-shot) | 25.04 | |
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| MMLU (5-shot) | 23.12 | |
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| TruthfulQA (0-shot) | 0.0 | |
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| Winogrande (5-shot) | 49.57 | |
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| GSM8K (5-shot) | 0.0 | |
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| DROP (3-shot) | 0.0 | |
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