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Model: budecosystem/code-millenials-13b Source: Original Platform
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---
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license: llama2
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library_name: transformers
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tags:
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- code
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model-index:
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- name: Code Millenials
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results:
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- task:
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type: text-generation
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dataset:
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type: openai_humaneval
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name: HumanEval
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.7621
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verified: false
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---
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# Bud Code Millenials 13B
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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
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### News 🔥🔥🔥
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- [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).
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- [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).
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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 (+) | MBPP (+) |
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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 (75) | 74.68 (62.9) |
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|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 (69.5) | 70.17 (57.6) |
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|Code Millenials 3B | <a href="https://huggingface.co/budecosystem/code-millenials-3b" target="_blank">HF Link</a> | 56.09 (52.43) | 55.13 (47.11) |
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|Code Millenials 1B | <a href="https://huggingface.co/budecosystem/code-millenials-1b" target="_blank">HF Link</a> | 51.82 (48.17) | 53.13 (44.61) |
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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-13b")
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model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-13b")
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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 8 A100 80GB for approximately 15hrs.
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| Hyperparameters | Value |
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| :----------------------------| :-----: |
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| per_device_train_batch_size | 2 |
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| gradient_accumulation_steps | 1 |
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| epoch | 3 |
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| steps | 34503 |
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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 | 8 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.
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"▁<EOT>": 32003,
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}
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BIN
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BIN
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|
After Width: | Height: | Size: 45 KiB |
30
special_tokens_map.json
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30
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"▁<EOT>"
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"content": "▁<EOT>",
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"▁<EOT>"
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"eot_token": "▁<EOT>",
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Reference in New Issue
Block a user