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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+PhiMerge-2.7B-Dare-daser - bnb 8bits
+- Model creator: https://huggingface.co/johnsnowlabs/
+- Original model: https://huggingface.co/johnsnowlabs/PhiMerge-2.7B-Dare-daser/
+
+
+
+
+Original model description:
+---
+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
+