---
base_model: microsoft/phi-2
datasets:
- teknium/OpenHermes-2.5
- ContextualAI/ultrafeedback_clair_32k
library_name: transformers
license: mit
pipeline_tag: text-generation
---

# phi-2-instruct-apo

This is a finetuned version of Microsoft's 2.7B parameter [phi-2](https://huggingface.co./microsoft/phi-2) transfromer model that has underwent a post-training process that incorporates both **supervised fine-tuning** and **anchored preference optimization** for instruction following. I used the [trl](https://huggingface.co./docs/trl/en/index) library and a single **A100 40GB** GPU during both the SFT and APO steps.

- Supervised Fine-Tuning
  - SFT Model: [phi-2-sft](https://huggingface.co./rasyosef/phi-2-sft-openhermes-128k-v2)
  - Used 128,000 instruction, response pairs from the [teknium/OpenHermes-2.5](https://huggingface.co./datasets/teknium/OpenHermes-2.5) dataset

- Anchored Preference Optimization (APO)
  - LoRA Adapter: [phi-2-apo](https://huggingface.co./rasyosef/phi-2-apo)
  - Used 10,000 preference pairs from the [ContextualAI/ultrafeedback_clair_32k](https://huggingface.co./datasets/ContextualAI/ultrafeedback_clair_32k) dataset

## How to use
### Chat Format

Given the nature of the training data, the phi-2 instruct model is best suited for prompts using the chat format as follows. 
You can provide the prompt as a question with a generic template as follows:
```markdown
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Question?<|im_end|>
<|im_start|>assistant
```

For example:
```markdown
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
How to explain Internet for a medieval knight?<|im_end|>
<|im_start|>assistant
```
where the model generates the text after `<|im_start|>assistant` .

### Sample inference code

This code snippets show how to get quickly started with running the model on a GPU:

```python
import torch 
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 

torch.random.manual_seed(0) 

model_id = "rasyosef/phi-2-instruct-apo"
model = AutoModelForCausalLM.from_pretrained( 
    model_id,  
    device_map="cuda",  
    torch_dtype="auto" 
) 

tokenizer = AutoTokenizer.from_pretrained(model_id) 

messages = [ 
    {"role": "system", "content": "You are a helpful AI assistant."}, 
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, 
] 

pipe = pipeline( 
    "text-generation", 
    model=model, 
    tokenizer=tokenizer, 
) 

generation_args = { 
    "max_new_tokens": 256, 
    "return_full_text": False, 
    "temperature": 0.0, 
    "do_sample": False, 
} 

output = pipe(messages, **generation_args) 
print(output[0]['generated_text'])  
```

Note: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_