budecosystem / code-millenials-34b

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 [email protected]

News 🔥🔥🔥

• [2024/01/09] We released Code Millenials 3B , which achieves the 56.09 pass@1 on the HumanEval Benchmarks .
• [2024/01/09] We released Code Millenials 1B , which achieves the 51.82 pass@1 on the HumanEval Benchmarks .
• [2024/01/03] We released Code Millenials 34B , which achieves the 80.48 pass@1 on the HumanEval Benchmarks .
• [2024/01/02] We released Code Millenials 13B , which achieves the 76.21 pass@1 on the HumanEval Benchmarks .

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 , DeepseekCoder , Gemini etc.

Models

🚀 Quick Start

Inference code using the pre-trained model from the Hugging Face model hub

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 = <Your code instruction here>

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.

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

Detailed results can be found here