Update README.md

README.md

@@ -1,122 +1,122 @@
----
-language: en
-license: mit
-tags:
-  - gpt
-  - transformer
-  - small-model
-  - from-scratch
-  - babymodel
-datasets:
-  - roneneldan/TinyStories
-library_name: transformers
-pipeline_tag: text-generation
----
-
-
-# 🍼 BabyLangModel
-
-A tiny GPT-style language model trained from scratch on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset. Built using PyTorch and a custom architecture inspired by [nanoGPT](https://github.com/karpathy/nanoGPT). This model was trained for 200k iterations on a consumer GPU (RTX 4060) using custom code from scratch.
-
----
-
-## 🧠 Model Details
-
-- **Architecture**: GPT (custom implementation)
-- **Parameters**: ~10–15M
-- **Layers**: 6
-- **Heads**: 6
-- **Embedding Size**: 384
-- **Block Size**: 128
-- **Tokenizer**: GPT-2 (`tiktoken`)
-- **Training Steps**: 200,000
-- **Training Loss**: ~1.80
-
----
-
-## 📚 Training Data
-
-We trained on the open-source **[TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)** dataset by Microsoft Research. It's a dataset of short, simple English stories written for young children (ages 2–4).
-
-- Clean, simple narratives
-- Ideal for small model generalization
-- 100% open and publicly available
-
----
-
-## 🧰 Usage (with `transformers`)
-
-This model uses a **custom architecture**, so you need to use `trust_remote_code=True`:
-
-```python
-from transformers import AutoModel
-
-model = AutoModel.from_pretrained("Exquisique/BabyLangModel", trust_remote_code=True)
-```
-
----
-
-## ✨ Sample Generation
-
-```text
-Prompt: Once upon a time there was a tiny robot who
-
-Output: ...lived in a far away home. One day, a little girl named Lily decided to go on a special trip in the forest. She walked and walked until she got there but suddenly she started to go. Her mom called her and said, "Don't worry, Lily. We will get you my special ride."
-```
-
-> 🗣️ Still improving, but quite readable and story-like after 200k iterations!
-
----
-
-## 💻 Train It Yourself
-
-You can find the full training code on [GitHub](https://github.com/Exquisique/Babymodel) or use this structure:
-
-```bash
-python -m src.tokenizer      # Tokenize TinyStories
-python -m src.train          # Train model from scratch
-python -m src.generate       # Generate text
-```
-
-You’ll also find:
-- Checkpointing & resume support
-- Configurable hyperparams
-- Gradient accumulation & mixed precision
-
----
-
-## 🔧 Config Used
-
-```json
-{
-  "vocab_size": 50257,
-  "block_size": 128,
-  "n_layer": 6,
-  "n_head": 6,
-  "n_embd": 384,
-  "model_type": "gpt"
-}
-```
-
----
-
-## 📦 Inference Notes
-
-To load the model, use:
-
-```python
-from transformers import AutoModel
-model = AutoModel.from_pretrained("Exquisique/BabyLangModel", trust_remote_code=True)
-```
-
-You can also upload a tokenizer later for full text input support (e.g. with `tiktoken`).
-
----
-
-## 🧑‍💻 Author
-**Exquisique** — GenAI explorer, poetic dreamer, and neural model whisperer.
-
----
-
-## 📜 License
-MIT — open source, fine-tune and remix freely. ✨
+---
+language: en
+license: mit
+tags:
+  - gpt
+  - transformer
+  - small-model
+  - from-scratch
+  - babymodel
+datasets:
+  - roneneldan/TinyStories
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+
+# BabyLangModel
+
+A tiny GPT-style language model trained from scratch on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset. Built using PyTorch and a custom architecture inspired by [nanoGPT](https://github.com/karpathy/nanoGPT). This model was trained for 200k iterations on a consumer GPU (RTX 4060) using custom code from scratch.
+
+---
+
+## 🧠 Model Details
+
+- **Architecture**: GPT (custom implementation)
+- **Parameters**: ~10–15M
+- **Layers**: 6
+- **Heads**: 6
+- **Embedding Size**: 384
+- **Block Size**: 128
+- **Tokenizer**: GPT-2 (`tiktoken`)
+- **Training Steps**: 200,000
+- **Training Loss**: ~1.80
+
+---
+
+## 📚 Training Data
+
+We trained on the open-source **[TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)** dataset by Microsoft Research. It's a dataset of short, simple English stories written for young children (ages 2–4).
+
+- Clean, simple narratives
+- Ideal for small model generalization
+- 100% open and publicly available
+
+---
+
+## 🧰 Usage (with `transformers`)
+
+This model uses a **custom architecture**, so you need to use `trust_remote_code=True`:
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("Exquisique/BabyLangModel", trust_remote_code=True)
+```
+
+---
+
+## ✨ Sample Generation
+
+```text
+Prompt: Once upon a time there was a tiny robot who
+
+Output: ...lived in a far away home. One day, a little girl named Lily decided to go on a special trip in the forest. She walked and walked until she got there but suddenly she started to go. Her mom called her and said, "Don't worry, Lily. We will get you my special ride."
+```
+
+> 🗣️ Still improving, but quite readable and story-like after 200k iterations!
+
+---
+
+## 💻 Train It Yourself
+
+You can find the full training code on [GitHub](https://github.com/Exquisique/Babymodel) or use this structure:
+
+```bash
+python -m src.tokenizer      # Tokenize TinyStories
+python -m src.train          # Train model from scratch
+python -m src.generate       # Generate text
+```
+
+You’ll also find:
+- Checkpointing & resume support
+- Configurable hyperparams
+- Gradient accumulation & mixed precision
+
+---
+
+## 🔧 Config Used
+
+```json
+{
+  "vocab_size": 50257,
+  "block_size": 128,
+  "n_layer": 6,
+  "n_head": 6,
+  "n_embd": 384,
+  "model_type": "gpt"
+}
+```
+
+---
+
+## 📦 Inference Notes
+
+To load the model, use:
+
+```python
+from transformers import AutoModel
+model = AutoModel.from_pretrained("Exquisique/BabyLangModel", trust_remote_code=True)
+```
+
+You can also upload a tokenizer later for full text input support (e.g. with `tiktoken`).
+
+---
+
+## 🧑‍💻 Author
+**Exquisique** — GenAI explorer, poetic dreamer, and neural model whisperer.
+
+---
+
+## 📜 License
+MIT — open source, fine-tune and remix freely. ✨