--- license: llama2 library_name: transformers tags: - code model-index: - name: Code Millenials results: - task: type: text-generation dataset: name: HumanEval type: openai_humaneval metrics: - type: pass@1 value: 0.8048 name: pass@1 verified: false - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 49.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 75.09 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 49.28 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 45.37 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 69.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 32.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard --- # Bud Code Millenials 34B 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 🔥🔥🔥 - [2024/01/09] 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

CodeMillenials

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 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-34b") model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-34b") template = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### 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 16 A100 80GB for approximately 50hrs. | Hyperparameters | Value | | :----------------------------| :-----: | | per_device_train_batch_size | 16 | | gradient_accumulation_steps | 1 | | epoch | 3 | | steps | 2157 | | learning_rate | 2e-5 | | lr schedular type | cosine | | warmup ratio | 0.1 | | optimizer | adamw | | fp16 | True | | GPU | 16 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. # [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_budecosystem__code-millenials-34b) | Metric |Value| |---------------------------------|----:| |Avg. |53.51| |AI2 Reasoning Challenge (25-Shot)|49.83| |HellaSwag (10-Shot) |75.09| |MMLU (5-Shot) |49.28| |TruthfulQA (0-shot) |45.37| |Winogrande (5-shot) |69.06| |GSM8k (5-shot) |32.45|