---
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- dpo
- rlhf
- trl
- autoquant
- gguf
---

# Llama3-8B-SuperNova-Spectrum-Hermes-DPO

This model is a **DPO fine-tuned** version of my `DARE_TIES` merged Model [`yuvraj17/Llama3-8B-SuperNova-Spectrum-dare_ties`](https://huggingface.co/yuvraj17/Llama3-8B-SuperNova-Spectrum-dare_ties) on the [yuvraj17/chatml-OpenHermes2.5-dpo-binarized-alpha-2k](https://huggingface.co/datasets/yuvraj17/chatml-OpenHermes2.5-dpo-binarized-alpha-2k) dataset.

## DPO (Direct Preference Optimization):

Direct Preference Optimization (DPO) is a fine-tuning technique that focuses on aligning a model's responses with human preferences or ranking data without requiring reinforcement learning steps, like in RLHF. 

<figure>

  <img src="https://cdn-uploads.huggingface.co/production/uploads/66137d95e8d2cda230ddcea6/kHcU5dkcSVqxEIWt_GRUB.png" width="1000" height="768">
  <figcaption> DPO vs RLHF <a href="//arxiv.org/abs/2305.18290">Reference</a> </figcaption>

</figure>

## Training:

- Trained on **1x A40s (48GB VRAM)** using the [HuggingFace TRL](https://huggingface.co/docs/trl/index).
- **QLoRA**(`4-bit precision`) for 1 epoch
  ```
  # LoRA configuration
  peft_config = LoraConfig(
      r=32,
      lora_alpha=16,
      lora_dropout=0.05,
      bias="none",
      task_type="CAUSAL_LM",
      target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
  )
  ```
### Training Params

The following hyperparameters were used during training:
- learning_rate: 5e-05
- beta=0.1
- num_devices: 1
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training Time = **1:57:00** hours

### Weight & Biases Report

[Report-Link](https://api.wandb.ai/links/my-sft-team/d211juao)

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "yuvraj17/Llama3-8B-SuperNova-Spectrum-Hermes-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]

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"])
```

## 🏆 Evaluation Scores

Coming Soon