nanogpt-slm-instruct

nanoGPT SLM Instruct -- 123.849984 Million Parameters

Instruction fine-tuned Small Language Model, trained from scratch -> pretrained on 133 classic english fiction books -> SFT on Alpaca-format instructions.

Quick Start

Option 1: Run directly (downloads model + runs 5 examples)

pip install torch tiktoken huggingface_hub
python nanogpt_slm_instruct_inference.py

Option 2: Import and use ask() in your own code

# Import loads the model automatically (one-time download from HuggingFace)
from nanogpt_slm_instruct_inference import ask

## First time  execution will O/P prefed 5 examples with model responses
# Simple question
print(ask("What is the capital of France?"))
print()
# With input context
print(ask(
    instruction="Summarize the following text.",
    input_text="Machine learning enables systems to learn from data rather than being explicitly programmed."
))
print()
# Control generation
print(ask(
    "Write a short poem about the ocean.",
    temperature=1.0,    # higher = more creative
    top_k=100,          # wider sampling pool
    max_tokens=150      # longer output
))
print()

Option 3: Load weights manually

from huggingface_hub import hf_hub_download
import torch, tiktoken

repo_id= "nishantup/nanogpt-slm-instruct"
filename = "nanogpt_slm_instruct.pth"

model_path = hf_hub_download(repo_id=repo_id, filename=filename)

# Build model (full architecture in nanogpt_slm_instruct_inference.py)
from nanogpt_slm_instruct_inference import GPT, GPTConfig, generate, format_input

config = GPTConfig()
model = GPT(config)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

Model Details

Prompt Format

Below is an instruction that describes a task.

### Instruction:
{instruction}

### Response:

With optional input:

Below is an instruction that describes a task, paired with further context.

### Instruction:
{instruction}

### Input:
{input}

### Response:

Files

ask() API Reference

ask(instruction, input_text="", max_tokens=256, temperature=0.7, top_k=40)