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Nithinvalluri/
nanogpt-slm-instruct

Name: nanogpt-slm-instruct
Availability: InStock

instruction-tuned (SFT)

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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)

bash

pip install torch tiktoken huggingface_hub
python nanogpt_slm_instruct_inference.py

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

python

# 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

python

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

Attribute	Value
Parameters	123.849984
Architecture	nanoGPT (12 layers, 12 heads, 768 dim)
Context length	256 tokens
Tokenizer	tiktoken GPT-2 BPE (50,257 tokens)
Fine-tuning	Supervised (Alpaca format)
Framework	PyTorch

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

File	Description
`nanogpt_slm_instruct.pth`	SFT fine-tuned weights
`nanogpt_slm_instruct_inference.py`	Standalone inference script -- import and call `ask()`
`config.json`	Model configuration

`ask()` API Reference

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

Parameter	Default	Description
`instruction`	(required)	The task instruction
`input_text`	`""`	Optional additional context
`max_tokens`	`256`	Maximum tokens to generate
`temperature`	`0.7`	0.0 = greedy, 0.7 = balanced, 1.5 = creative
`top_k`	`40`	Top-k filtering (None = no filtering)

Nithinvalluri/nanogpt-slm-instruct