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 |