README.md

---
license: cc-by-nc-4.0
base_model: johnsnowlabs/CodeGemma-2B-Slerp
tags:
- generated_from_trainer
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: CodeGemma-2B-Slerp-dora
  results: []
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# CodeGemma-2B-Slerp-dora
![image/png](https://cdn-uploads.huggingface.co/production/uploads/660cfe98280a82e38fe4ef49/JrTnaEV4AapbLwx0Cb-Lc.png)

<!-- Provide a quick summary of what the model is/does. -->
CodeGemma-2B-Slerp-dora is a DPO fine-tuned of [johnsnowlabs/CodeGemma-2B-Slerp](https://huggingface.co/johnsnowlabs/CodeGemma-2B-Slerp) on [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset using DoRA. The model has been trained for 1080 steps. All hyperparams are given below.


## 🏆 Evaluation results

### Coming Soom

## Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johnsnowlabs/CodeGemma-2B-dora"
messages = [{"role": "user", "content": "Explain what is Machine learning."}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

### Training hyperparameters
The following hyperparameters were used during training:

- learning_rate: 2e-04
- train_batch_size: 1
- gradient_accumulation_steps: 8
- optimizer: PagedAdamW with 32-bit precision
- lr_scheduler_type: Cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1080


### LoRA Config

- lora_r: 16
- lora_alpha: 32
- lora_dropout: 0.05
- peft_use_dora: true

### Framework versions
- Transformers 4.39.0.dev0
- Peft 0.9.1.dev0
- Datasets 2.18.0
- torch 2.2.0
- accelerate 0.27.2