---
license: cc-by-nc-4.0
base_model: Johnsnowlabs/PhiMerge-2.7B-Dare
tags:
- generated_from_trainer
- Phi
- axolotl
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: PhiMerge-2.7B-Dare-daser
  results: []
datasets:
- argilla/distilabel-capybara-dpo-7k-binarized
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# PhiMerge-2.7B-Dare-daser 
![image/png](https://cdn-uploads.huggingface.co/production/uploads/660cfe98280a82e38fe4ef49/yToMeQHvr5CJPYxA5sdQc.png)

PhiMerge-2.7B-Dare-daser is a mixture of two techniques that are LaserQlora and Dora. This model is a DPO fine-tuned of [johnsnowlabs/PhiMerge-2.7B-Dare](https://huggingface.co/johnsnowlabs/PhiMerge-2.7B-Dare) using the [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized) preference dataset. The model has been trained on top 16 projections (q_proj, k_proj, v_proj) based on snr values. This model has been trained for 1080 steps.

## 🏆 Evaluation results

#### Coming Soon

## Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johnsnowlabs/PhiMerge-2.7B-Dare-daser"
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
- eval_batch_size: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: paged_adamw_32bit
- 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 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0