Upload README.md with huggingface_hub

README.md

@@ -16,7 +16,7 @@ tags:
 This repository provides ultra-lightweight Llama2 model files across various formats (both **GGUF** and **Hugging Face / Safetensors**), trained on the TinyStories dataset and optimized for compatibility with Andrej Karpathy's `llama2.c` and `llama.cpp`.
 
 ### Why this repository exists
-When developing a custom LLM inference engine from scratch (C/C++, Vulkan, WebAssembly, etc.) or testing custom hardware kernels, debugging with a full-sized model is slow. This suite offers a true **1M parameter scale model** (~1MB to ~4MB depending on the quantization format), allowing developers to validate their loaders, serialization, quantization kernels, and inference logic step-by-step with maximum efficiency.
+When developing a custom LLM inference engine, debugging with a full-sized model is slow. This suite offers a true **1M parameter scale model** (~1MB to ~4MB depending on the quantization format), allowing developers to validate their loaders, serialization, quantization kernels, and inference logic step-by-step with maximum efficiency.
 
 ---
 
@@ -68,8 +68,6 @@ To verify your local setup or compare tokens using the official native utilities
 
 The `model.bin` is fully compatible with the 512-vocab `tokenizer.bin` derived from the `stories260k` asset pipeline.
 
-> ⚠️ **Important Note for `llama2.c/run`:** When passing a prompt to the `run` binary, you must use the **`-i`** option. Do not use `-p`, as `-p` is reserved for the Top-p sampling threshold in `llama2.c`, which will cause the prompt to be ignored.
-
 ```bash
 ./run model.bin -z tokenizer.bin -i "Tom and Jerry are " -n 64
 
@@ -83,7 +81,7 @@ You can import the Hugging Face variant directly into Python using the `transfor
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-repo_id = "your-username/your-repo-name"
+repo_id = "shibatch/tiny1m"
 
 # The library automatically looks into the hf/ folder using the subfolder argument
 tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="hf")