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.
[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

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

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.

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

Detailed results can be found here

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

budecosystem
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code-millenials-34b