davanai

davanai 1

A LoRA adapter fine-tuned on Qwen/Qwen2.5-32B, developed by Vega Aiden Lab.

davanai 1 was trained across 37 datasets.

Model details

• Developed by: Vega Aiden Lab
• Model: davanai 1
• Base model: Qwen/Qwen2.5-32B
• Training data: 37 datasets
• Method: LoRA (PEFT)

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Run it locally — step by step

This is a LoRA adapter, not a full model. To run it you download the base model Qwen/Qwen2.5-32B and apply this adapter on top. The base model is large, so make sure your machine can handle it (see hardware note below).

1. Install the required libraries

pip install torch transformers peft accelerate safetensors

2. Log in to Hugging Face (needed to download the base model)

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 and paste this in

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen2.5-32B"
adapter = "emmaoba/davanai"

# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    device_map="auto",      # uses your GPU automatically
    torch_dtype="auto",
)

# Apply the davanai 1 adapter
model = PeftModel.from_pretrained(model, adapter)
model.eval()

# Chat with it
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 (this is large and happens only once — it's cached for next time), then prints davanai 1's reply.

Hardware note

Qwen2.5-32B is a 32-billion-parameter model. To run it in full precision you need a lot of GPU memory (roughly 64+ GB). If your GPU is smaller, load it in 4-bit instead — install bitsandbytes (pip install bitsandbytes) and change the model loading line to:

model = AutoModelForCausalLM.from_pretrained(
    base,
    device_map="auto",
    load_in_4bit=True,
)

This lets it run on a single 24 GB GPU (like an RTX 3090/4090).

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Training configuration

• Method: LoRA (PEFT)
• Rank (r): 32
• Alpha: 64
• Dropout: 0.05
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj