🧠 Nano-nano v4.5

📋 Quick Facts

🏗️ Architecture

Standard LLaMA decoder-only transformer. Scaled ~8.5% wider + 1 extra layer vs v4.

📊 Evaluation

Automatically evaluated after training across 5 capability dimensions.

Hallucination resistance — whether the model appropriately declines questions about future events, fictional entities, or impossible premises rather than confabulating.

👁 Category Scores
👁 Hallucination
👁 Training Curves

🍳 Training

Dataset Mix

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
 "ray0rf1re/Nano-nano_v4.5",
 torch_dtype="auto",
 device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("ray0rf1re/Nano-nano_v4.5")

def generate(prompt: str, max_new_tokens: int = 256) -> str:
 text = f"### Instruction:
{prompt}

### Response:
"
 inputs = tokenizer(text, return_tensors="pt").to(model.device)
 out = model.generate(
 **inputs,
 max_new_tokens = max_new_tokens,
 do_sample = True,
 temperature = 0.7,
 top_p = 0.9,
 repetition_penalty = 1.1,
 pad_token_id = tokenizer.eos_token_id,
 )
 new_ids = out[0][inputs["input_ids"].shape[-1]:]
 return tokenizer.decode(new_ids, skip_special_tokens=True).strip()

# Examples
print(generate("Write a Python function to reverse a linked list."))
print(generate("What is the capital of France?"))
print(generate("Explain gradient descent in simple terms."))

⚠️ Limitations

• Context limited to 1 024 tokens — unsuitable for long documents
• Trained on 0.08 B tokens — far less than production models
• May hallucinate on obscure or out-of-distribution queries
• Not RLHF/DPO aligned — outputs may vary in safety and tone
• Pascal GPU constraint (GTX 1080): fp32/fp16 only, no bf16

📜 Citation

@misc{nano-nano-v45,
 author = {ray0rf1re},
 title = {Nano-nano v4.5: Compact LLaMA-Family Causal LM},
 year = {2026},
 publisher = {HuggingFace},
 howpublished = {https://huggingface.co/ray0rf1re/Nano-nano_v4.5},
}