---
library_name: transformers
license: mit
datasets:
- teknium/OpenHermes-2.5
---

# microsoft/phi-2 + teknium/OpenHermes-2.5

## Training
* QLoRA rank 32, LR 2e-5, 1 epoch
* effective batch size: 200
* max. seq. length: 1024 tokens
* code in code/

## Evals

|                                   Model                                    |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|----------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[g-ronimo/phi-2-OpenHermes-2.5](https://huggingface.co/g-ronimo/phi-2-OpenHermes-2.5)|  30.27|  71.18|     43.87|    35.9|   45.3|
|[minghaowu/phi-2-OpenHermes-2.5](https://huggingface.co/minghaowu/phi-2-OpenHermes-2.5)|  27.95|  67.55|     48.07|   36.17|  44.94|
|[phi-2](https://huggingface.co/microsoft/phi-2)|  27.96|  70.84|     44.46|   35.17|  44.61|

## Inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

modelpath="g-ronimo/phi-2-OpenHermes-2.5"

model = AutoModelForCausalLM.from_pretrained(
    modelpath,    
    torch_dtype=torch.bfloat16,
    device_map="auto",
    # attn_implementation="flash_attention_2",
)
tokenizer = AutoTokenizer.from_pretrained(modelpath) 

messages = [
    {"role": "system", "content": "answer like a pirate"},
    {"role": "user", "content": "what does it mean to be successful?"},
]
        
input_tokens = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")
output_tokens = model.generate(input_tokens, max_new_tokens=500)
output = tokenizer.decode(output_tokens[0])

print(output)
```

>Ahoy there, matey! To me, being successful means having the wind in your sails and reaching the treasure you've been dreaming of. It's about setting sail on a journey with clear goals, working hard, facing challenges head-on, and never losing sight of what truly matters. So, set your compass right, hoist your Jolly Roger high, and let's embark on this adventure together! ⚓️💰⛵️