emmaoba/davanai-2

davanai 2

A LoRA adapter fine-tuned on microsoft/Phi-4-mini-instruct (3.8B), developed by Vega Aiden Lab.

Model details

• Developed by: Vega Aiden Lab
• Model: davanai 2
• Base model: microsoft/Phi-4-mini-instruct
• Method: LoRA (PEFT), rank 16

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Run it locally — step by step

This is a LoRA adapter, not a full model. You download the base model microsoft/Phi-4-mini-instruct and apply this adapter on top. Phi-4-mini is small (3.8B), so it runs on a normal GPU (8 GB+) or even on CPU if you're patient.

1. Install the libraries

pip install torch transformers peft accelerate safetensors

2. Log in to Hugging Face

pip install -U "huggingface_hub[cli]"
hf auth login

Paste a token from https://huggingface.co/settings/tokens when asked.

3. Create a file called run.py

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "microsoft/Phi-4-mini-instruct"
adapter = "emmaoba/davanai-2"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    device_map="auto",
    torch_dtype="auto",
)

# Apply the davanai 2 adapter
model = PeftModel.from_pretrained(model, adapter)
model.eval()

messages = [{"role": "user", "content": "Hello! Who are you?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

4. Run it

python run.py

The first run downloads the base model (once, then cached), then prints davanai 2's reply.

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Training configuration

• Method: LoRA (PEFT)
• Rank (r): 16
• Alpha: 32
• Dropout: 0.05
• Target modules: qkv_proj, o_proj, gate_up_proj, down_proj