README.md

metadata

license: apache-2.0
library_name: pytorch
tags:
  - language-model
  - causal-lm
  - gpt
  - from-scratch
  - educational
pipeline_tag: text-generation
framework: pytorch

SimBot GPT (Level 1)

SimBot GPT is a from-scratch GPT-style language model implemented in PyTorch. This project is focused on learning LLM internals, not on instruction tuning or production use.

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Model Overview

• Architecture: Decoder-only Transformer (GPT-like)
• Training Objective: Causal Language Modeling
• Dataset: Domain-specific text (Simdega / regional data)
• Purpose: Educational (understanding how LLMs work internally)

⚠️ This is a base language model, not instruction-tuned and not grounded with RAG.

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Repository Contents

• simbot.safetensors — model weights (safe & HF-recommended format)
• tokenizer.json — BPE tokenizer
• config.json — model hyperparameters
• model/simbot.py — model architecture (PyTorch)

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Requirements (Inference Only)

The following packages are required to load and run the model:

torch==2.9.1
tokenizers==0.22.1
safetensors

---

Usage Example

import json
from safetensors.torch import load_file
from tokenizers import Tokenizer
from model.simbot import SIMGPT

# Load tokenizer
tokenizer = Tokenizer.from_file("tokenizer.json")

# Load config
with open("config.json") as f:
    cfg = json.load(f)

# Build model
model = SIMGPT(
    vocab_size=cfg["vocab_size"],
    block_size=cfg["block_size"],
    n_layers=cfg["n_layers"],
    n_heads=cfg["n_heads"],
    d_model=cfg["d_model"]
)

# Load weights
state_dict = load_file("simbot.safetensors")
model.load_state_dict(state_dict)
model.eval()

Prompting the Model

This model is a custom PyTorch implementation and does not support the Hugging Face inference widget.

Interactive Usage (Recommended)

python inference.py