NAME0x0/AVA-v2

AVA v2

AVA v2 is a QLoRA fine-tune of Qwen/Qwen3.5-2B that achieves 79% on ARC-Challenge and 48% on GSM8K while training and running inference in under 2 GB of VRAM.

Trained entirely on a single NVIDIA RTX A2000 Laptop GPU (4 GB VRAM). The adapter is 42 MB.

Results

Benchmark	Qwen3.5-2B Base	AVA v2	Improvement
ARC-Challenge (100)	66.0%	79.0%	+13.0pp
GSM8K (50)	28.0%	48.0%	+20.0pp

Comparison to Other Small Models

Model	Params	ARC-C	GSM8K
Gemma 2 2B	2.0B	55.7%	24.3%
SmolLM2-1.7B-Instruct	1.7B	~52%	48.2%
Llama 3.2 1B-Instruct	1.0B	59.4%	44.4%
Llama 3.2 3B-Instruct	3.0B	78.6%	77.7%
AVA v2	2.0B	79.0%	48.0%

AVA v2's ARC-Challenge score at 2B parameters exceeds Llama 3.2 3B-Instruct (78.6% at 3B).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3.5-2B",
    quantization_config=bnb_config,
    device_map="auto",
    dtype=torch.bfloat16,
    attn_implementation="sdpa",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-2B")

model = PeftModel.from_pretrained(model, "NAME0x0/AVA-v2")
model = model.merge_and_unload()

messages = [{"role": "user", "content": "Explain why ice floats on water."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Training Details

• Method: QLoRA (4-bit NF4 + LoRA rank 16)
• Base model: Qwen3.5-2B
• Training data: 20,741 prompt-response pairs (math, science, reasoning, instruction following)
• Hardware: NVIDIA RTX A2000 Laptop (4 GB VRAM)
• Training time: 100.5 minutes
• Final loss: 0.4145
• Peak VRAM: 1.81 GB
• Trainable params: 10,911,744 / 1,892,736,832 (0.58%)
• Optimizer: paged_adamw_8bit
• LR schedule: cosine, peak 1.5e-4
• Batch size: 1 (gradient accumulation 8, effective batch 8)
• Max sequence length: 384 tokens
• Epochs: 1

Limitations

• Evaluation was run on 100 ARC-Challenge and 50 GSM8K items (not full test sets)
• Evaluation protocols (shot count, prompting) differ across model comparison sources
• The model inherits Qwen3.5-2B's base capabilities and limitations
• Max training sequence length was 384 tokens due to VRAM constraints

Citation

@misc{ava-v2-2026,
  title={AVA v2: QLoRA Fine-tuning Under Extreme VRAM Constraints},
  author={Afsah},
  year={2026},
  url={https://github.com/NAME0x0/AVA}
}