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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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- TokenBender/code_instructions_122k_alpaca_style |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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base_model: codellama/CodeLlama-13b-Instruct-hf |
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--- |
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<p align="center" style="font-size:34px;"><b>Panda-Coder 🐼</b></p> |
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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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## Inference |
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```python |
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import torch |
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import transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments,BitsAndBytesConfig |
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prompt = f"""### Instruction: |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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Write a Python quickstart script to get started with TensorFlow |
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### Input: |
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### Response: |
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""" |
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input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda() |
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outputs = base_model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, top_p=0.9,temperature=0.1,repetition_penalty=1.1) |
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print(f"Output:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}") |
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``` |
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Output |
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```bash |
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Output: |
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import tensorflow as tf |
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# Create a constant tensor |
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hello_constant = tf.constant('Hello, World!') |
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# Print the value of the constant |
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print(hello_constant) |
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``` |
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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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### 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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### Citation |
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``` |
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@misc {lucifertrj, |
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author = { {Tarun Jain} }, |
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title = { Panda Coder-13B by AI Planet}, |
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year = 2023, |
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url = { https://huggingface.co/aiplanet/panda-coder-13B }, |
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publisher = { Hugging Face } |
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} |
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``` |
