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*This model was contributed to Hugging Face Transformers on 2025-11-27.*

# NanoChat


[NanoChat](https://huggingface.co/karpathy/nanochat-d32) is a compact decoder-only transformer model designed for educational purposes and efficient training. The model features several fundamental architectural innovations which are common in modern transformer models. Therefore, it is a good model to use as a starting point to understand the principles of modern transformer models. NanoChat is a variant of the [Llama](https://huggingface.co/docs/transformers/en/model_doc/llama) architecture, with simplified attention mechanism and normalization layers.

The architecture is based on [nanochat](https://github.com/karpathy/nanochat) by [Andrej Karpathy](https://huggingface.co/karpathy), adapted for the Hugging Face Transformers library by [Ben Burtenshaw](https://huggingface.co/burtenshaw).

> [!TIP]
> This model was contributed by the Hugging Face team.

The example below demonstrates how to use NanoChat for text generation with chat templates.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


chatbot = pipeline(
    task="text-generation",
    model="karpathy/nanochat-d32",
    device=0
)

conversation = [
    {"role": "user", "content": "What is the capital of France?"},
]

outputs = chatbot(conversation, max_new_tokens=64)
print(outputs[0]["generated_text"][-1]["content"])
```

</hfoption>
<hfoption id="AutoModel">

```python
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


model_id = "karpathy/nanochat-d32"

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

conversation = [
    {"role": "user", "content": "What is the capital of France?"},
]

inputs = tokenizer.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=64,
    )

# Decode only the generated tokens (excluding the input prompt)
generated_tokens = outputs[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated_tokens, skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## NanoChatConfig

[[autodoc]] NanoChatConfig

## NanoChatModel

[[autodoc]] NanoChatModel
    - forward

## NanoChatForCausalLM

[[autodoc]] NanoChatForCausalLM
    - forward
