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README.md

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+---
+library_name: peft
+base_model: Qwen/Qwen3.5-2B
+license: apache-2.0
+tags:
+  - qlora
+  - 4bit
+  - low-resource
+  - arc-challenge
+  - gsm8k
+  - science
+  - math
+  - reasoning
+datasets:
+  - custom
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# AVA v2
+
+AVA v2 is a QLoRA fine-tune of [Qwen/Qwen3.5-2B](https://huggingface.co/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
+
+```python
+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}
+}
+```