• ✅ Build a Base LLM (done)
• ✅ Turn it into an Instruct Model (done)
• Package it as a Hugging Face Model
  • config.json
  • generation_config.json
  • tokenizer.json
  • tokenizer_config.json
  • special_tokens_map.json
  • model.safetensors
  • README.md
• Implement from_pretrained() Compatibility
  • Make it load with the same API style as Qwen.
• Publish to Hugging Face Hub
  • harsh/my-gpt2-instruct
• Use It Like Any Other Model
  • use it as MyGPT2ForCausalLM.from_pretrained(...)
  • Then change it to use transformers library AutoModelForCausalLM.from_pretrained(...) Side Quest- make the model a gguf Main Quest finished, LLM can now be used as

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "harsh/my-gpt2-instruct"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

model.eval()

# User question
question = "What is the capital of France?"

# Tokenize
inputs = tokenizer(question, return_tensors="pt")

# Generate
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id,
    )

# Decode
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

full roadmap : ✅ GPT-2 from scratch ✅ Instruction tuning ✅ LLM packaging (current) ⬜ Hugging Face compatibility ⬜ Publish to Hub

========================= LLM Journey Complete

↓ Swap permanently to Qwen2.5-0.5B-Instruct ↓ AI Engineering ├── Model Usage │ ├── Prompt Engineering │ ├── Structured Outputs │ ├── Tool Calling │ ├── Streaming │ └── Chat Templates │ ├── Model Enhancement │ ├── RAG ✅ (already implemented) │ ├── LoRA / QLoRA (optional) │ └── Quantization (optional) │ ├── AI Systems │ ├── LangChain │ ├── LlamaIndex │ ├── Haystack │ ├── Agents │ ├── MCP │ ├── Multi-Agent │ └── Workflows │ ├── Infrastructure │ ├── FastAPI │ ├── vLLM │ ├── Ollama │ ├── Docker │ └── Deployment │ └── Evaluation ├── RAG Evaluation ├── Hallucination ├── Latency ├── Cost └── Benchmarks

![[diagram-export-04-08-2026-13_57_38.png|883]]