What Are the Main Stages of LLM Training Covered by nanochat?

nanochat does not actually implement any LLM training stages; it is an inference‑only repository that covers the four core pipeline steps required to serve a pre‑trained model: loading weights, tokenizing input, generating tokens via forward passes, and streaming the output.

Unlike full training frameworks, Andrej Karpathy’s nanochat is deliberately minimal—it demonstrates how to chat with an already‑trained transformer rather than how to train one. The repository assumes you have a finished checkpoint (e.g., from Hugging Face or nanoGPT) and focuses exclusively on the runtime workflow that turns prompts into streaming text.

Why nanochat Skips Traditional Training Stages

Traditional LLM development involves three major training phases: pre‑training on massive corpora, supervised fine‑tuning (SFT) on instruction data, and reinforcement learning from human feedback (RLHF). According to the source structure, nanochat contains no loss‑computation loops, no back‑propagation utilities, and no data loaders for training batches. Instead, the project is designed as a lightweight wrapper around a frozen model weights file.

If you need a reference implementation of actual training stages, Karpathy’s nanoGPT repository provides the pre‑training and fine‑tuning logic; nanochat simply consumes the artifacts produced by such systems.

The Four Main Pipeline Stages nanochat Implements

While nanochat omits training, it does implement the complete inference pipeline across four well‑defined stages. Each stage maps to a specific source file in the repository.

Model Loading

The first stage downloads and initializes the pre‑trained weights. In nanochat/main.py, the application loads a Hugging Face transformer checkpoint into memory, moves it to the appropriate device (CPU or CUDA), and prepares the model for evaluation mode.

# Conceptual flow from nanochat/main.py

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gpt2")
model.eval()  # Inference mode only; no gradient computation

This step corresponds to deployment preparation rather than training, ensuring the frozen parameters are ready for forward passes.

Tokenization

Before generation can begin, user prompts must be converted into token IDs. The nanochat/tokenizer.py file provides a thin wrapper around the Hugging Face tokenizer, handling the text‑to‑integer mapping and attention mask creation.

# From nanochat/tokenizer.py pattern

tokens = tokenizer.encode(prompt, return_tensors="pt")
input_ids = tokens.to(device)

This stage bridges human‑readable text and the numerical input space the transformer expects, but it does not involve any parameter updates or training‑data processing.

Forward Pass and Sampling

The core generation logic lives in nanochat/generation.py, specifically within the generate() method. This stage runs the forward pass to obtain logits, applies temperature scaling, and samples the next token using top‑k or top‑p filtering.

# Representative pattern from nanochat/generation.py

def generate(self, input_ids, max_new_tokens, temperature=0.8):
    for _ in range(max_new_tokens):
        logits = model(input_ids).logits[:, -1, :] / temperature
        probs = torch.softmax(logits, dim=-1)
        next_token = torch.multinomial(probs, num_samples=1)
        input_ids = torch.cat([input_ids, next_token], dim=1)
        yield next_token

This is purely inference‑time computation; the weights remain frozen throughout the loop.

Streaming Output

Finally, nanochat/ui.py implements the stream_response() function, which decodes generated token IDs back to text in real time and pushes them to the web interface via streaming chunks. This stage handles output rendering and user experience, completing the inference pipeline without ever touching training loss functions.

# From nanochat/ui.py conceptual flow

for token_id in generator.generate(input_ids):
    text_chunk = tokenizer.decode(token_id, skip_special_tokens=True)
    yield text_chunk

Complete Inference Example

Here is a practical example showing how nanochat orchestrates these four stages to serve a model locally:

from nanochat import NanoChat

# Stage 1: Model Loading

chat = NanoChat(model_name="gpt2", device="cuda")

# Stages 2-4: Tokenize, Generate, and Stream

prompt = "Explain the difference between AI training and inference."
for chunk in chat.stream(prompt, temperature=0.7, max_new_tokens=256):
    print(chunk, end="", flush=True)

Key details:

• NanoChat() initializes the tokenizer and model weights (Stages 1 & 2).
• chat.stream() wraps the generation loop and decoding (Stages 3 & 4).
• No optimizer states or gradient buffers are allocated, confirming the absence of training.

Summary

• nanochat is inference‑only: It does not implement pre‑training, fine‑tuning, or RLHF stages.
• Four pipeline stages: The repository covers model loading (main.py), tokenization (tokenizer.py), forward pass and sampling (generation.py), and streaming output (ui.py).
• No back‑propagation: The codebase explicitly operates in eval() mode and yields tokens without computing losses or updating weights.
• Contrast with nanoGPT: For actual LLM training stages (pre‑training and fine‑tuning), refer to the nanoGPT repository; nanochat strictly consumes finished checkpoints.

Frequently Asked Questions

Does nanochat support fine‑tuning or RLHF?

No. nanochat does not contain training loops, loss functions, or optimizers. It loads frozen model weights and runs inference only. If you require fine‑tuning or reinforcement learning from human feedback, you need a training framework such as nanoGPT, transformers.Trainer, or alignment libraries like TRL.

What is the difference between nanochat and nanoGPT?

nanoGPT is a minimal implementation of a GPT‑style model that includes training code (pre‑training and fine‑tuning), whereas nanochat is a minimal serving interface that demonstrates how to chat with an existing model. Think of nanoGPT as the factory and nanochat as the showroom.

Can I use nanochat with my own fine‑tuned model?

Yes. Since nanochat uses standard Hugging Face model loading via AutoModelForCausalLM.from_pretrained(), you can point it to any local directory or Hugging Face Hub repository containing a compatible transformer checkpoint, including fine‑tuned variants (e.g., LoRA adapters merged into base weights).

Does nanochat implement the tokenization stage differently during training?

No, because nanochat never enters a training phase. Its tokenization logic (found in nanochat/tokenizer.py) is used exclusively to encode prompts for the forward pass and decode output tokens for the user interface. The tokenizer remains frozen and does not update its vocabulary or embeddings.

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