# Bud Code Millenials 8B Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks. Bud Millenial Code Gen open-source models are currently the State of the Art (SOTA) for code generation, beating all the existing models of all sizes. We have achieved a HumanEval value of 80.48 @ Pass 1, beating proprietary models like Gemini Ultra, Claude, GPT-3.5 etc. by a large margin, and on par with GPT-4 (HumanEval ~ 82. Ref. WizardCoder). Our proprietary model (Bud Code Jr) beats GPT-4 as well with a HumanEval value of 88.2 & a context size of 168K, we will be releasing an API for Researchers, Enterprises, and potential Partners by January 2024 end. If interested, please reach out to jithinvg@bud.studio ### News 🔥🔥🔥 - [2024/04/21] We released **Code Millenials 8B** , which achieves the **67.1 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). - [2024/01/09] We released **Code Millenials 3B** , which achieves the **56.09 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). - [2024/01/09] We released **Code Millenials 1B** , which achieves the **51.82 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). - [2024/01/03] We released **Code Millenials 34B** , which achieves the **80.48 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). - [2024/01/02] We released **Code Millenials 13B** , which achieves the **76.21 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). ### HumanEval

For the millenial models, the eval script in the github repo is used for the above result. Note: The humaneval values of other models are taken from the official repos of [WizardCoder](https://github.com/nlpxucan/WizardLM), [DeepseekCoder](https://github.com/deepseek-ai/deepseek-coder), [Gemini](https://deepmind.google/technologies/gemini/#capabilities) etc. ### Models | Model | Checkpoint | HumanEval (+) | MBPP (+) | |---------|-------------|---------------|----------| |Code Millenials 34B | HF Link | 80.48 (75) | 74.68 (62.9) | |Code Millenials 13B | HF Link | 76.21 (69.5) | 70.17 (57.6) | |Code Millenials 8B | HF Link | 67.1 (61.6) | - | |Code Millenials 3B | HF Link | 56.09 (52.43) | 55.13 (47.11) | |Code Millenials 1B | HF Link | 51.82 (48.17) | 53.13 (44.61) | ### 🚀 Quick Start Inference code using the pre-trained model from the Hugging Face model hub ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-8b") model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-8b") template = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. ### Instruction: {instruction} ### Response:""" instruction = prompt = template.format(instruction=instruction) inputs = tokenizer(prompt, return_tensors="pt") sample = model.generate(**inputs, max_length=128) print(tokenizer.decode(sample[0])) ``` ## Training details The model is trained of 8 A100 80GB for approximately 50hrs. | Hyperparameters | Value | | :----------------------------| :-----: | | per_device_train_batch_size | 8 | | gradient_accumulation_steps | 1 | | epoch | 3 | | steps | 8628 | | learning_rate | 2e-5 | | lr schedular type | cosine | | warmup ratio | 0.1 | | optimizer | adamw | | fp16 | True | | GPU | 8 A100 80GB | ### Important Note - **Bias, Risks, and Limitations:** Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.

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