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--- |
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license: llama2 |
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metrics: |
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- code_eval |
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library_name: transformers |
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tags: |
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- code |
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--- |
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# Introducing Code Millenials 34B |
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Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks, aiming to revolutionize how systems understand and translate natural language instructions into code queries. Built on CodeLLaMa Python 34B, our model has been meticulously fine-tuned with a curated code generation instructions, ensuring quality and precision. |
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### News 🔥🔥🔥 |
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- [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). |
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- [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). |
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### HumanEval |
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<p align="center" width="100%"> |
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<a ><img src="https://raw.githubusercontent.com/BudEcosystem/code-millenials/main/assets/result.png" alt="CodeMillenials" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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For the millenial models, the eval script in the github repo is used for the above result. |
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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. |
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### Models |
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| Model | Checkpoint | HumanEval | |
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|---------|-------------|-----------| |
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|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 | |
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|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 | |
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### 🚀 Quick Start |
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Inference code using the pre-trained model from the Hugging Face model hub |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-34b") |
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model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-34b") |
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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. |
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### Instruction: {instruction} ### Response:""" |
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instruction = <Your code instruction here> |
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prompt = template.format(instruction=instruction) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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sample = model.generate(**inputs, max_length=128) |
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print(tokenizer.decode(sample[0])) |
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``` |
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## Training details |
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The model is trained of 16 A100 80GB for approximately 50hrs. |
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| Hyperparameters | Value | |
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| :----------------------------| :-----: | |
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| per_device_train_batch_size | 16 | |
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| gradient_accumulation_steps | 1 | |
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| epoch | 3 | |
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| steps | 2157 | |
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| learning_rate | 2e-5 | |
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| lr schedular type | cosine | |
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| warmup ratio | 0.1 | |
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| optimizer | adamw | |
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| fp16 | True | |
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| GPU | 16 A100 80GB | |
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### Important Note |
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- **Bias, Risks, and Limitations:** Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. |
