metadata
license: llama2
Introducing Code Millenials 13B
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 13B, our model has been meticulously fine-tuned with a curated code generation instructions, ensuring quality and precision.
News 🔥🔥🔥
- [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 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-13b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-13b")
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 8 A100 80GB for approximately 15hrs.
| Hyperparameters | Value |
|---|---|
| per_device_train_batch_size | 2 |
| gradient_accumulation_steps | 1 |
| epoch | 3 |
| steps | 19206 |
| learning_rate | 2e-5 |
| lr schedular type | cosine |
| warmup ratio | 0.1 |
| optimizer | adamw |
| fp16 | True |
| GPU | 8 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.