Update README.md

README.md

@@ -1,16 +1,17 @@
----
-license: apache-2.0
-language:
-- en
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- i3-arhitecture
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- i3-architecture
+- custom_code
+---
 
 # i3-tiny
 
-**i3-tiny** is a compact, efficient character-level language model designed for experimentation and exploration in text generation. Despite its small size, it packs a surprising punch for creative and research-oriented tasks, generating sequences that are quirky, unpredictable, and full of “human-like” character-level errors.
+**i3-tiny** is a compact, efficient character-level language model designed for experimentation and exploration in text generation. Despite its small size, it can generate sequences that are quirky, unpredictable, and full of “human-like” character-level errors.
 
 ---
 
@@ -18,27 +19,29 @@ tags:
 
 i3-tiny is trained to predict the next character in a sequence, making it ideal for **character-level language modeling**, **creative text generation**, and **research on lightweight, efficient models**. Its small footprint allows rapid experimentation, even on modest hardware, and it provides a playground for studying how models learn patterns in sequences of characters.
 
-The model is **intentionally experimental** — it’s not aligned, fact-checked, or polished. Instead, it showcases how a compact architecture can capture patterns in text, learn from repetition, and generate outputs that are sometimes surprisingly coherent, sometimes hilariously garbled.
+The model is **intentionally experimental** — it’s not aligned, fact-checked, or polished. Outputs may be coherent, partially readable, or amusingly garbled.
 
 ---
 
 ## Training Details
 
-* **Dataset:** ~45,830 characters (a curated text corpus repeated to improve exposure)
-* **Vocabulary:** 34 characters (all lowercased)
-* **Sequence length:** 128
-* **Training iterations:** 2,000
-* **Batch size:** 2
-* **Optimizer:** AdamW, learning rate 3e-4
-* **Model parameters:** 711,106
+* **Dataset:** ~45,830 characters (a curated text corpus repeated for exposure)  
+* **Vocabulary:** 34 characters (all lowercased)  
+* **Sequence length:** 128  
+* **Training iterations:** 2,000  
+* **Batch size:** 2  
+* **Optimizer:** AdamW, learning rate 3e-4  
+* **Model parameters:** 711,106  
 * **Performance notes:** Each iteration takes roughly 400–500 ms; 100 iterations take ~45 s on average. Loss steadily decreased from 3.53 to 2.15 over training.
 
 **Example generation (iteration 1200):**
 
 ```
+
 Prompt: "The quick"
 Generated: the quick efehn. dethe cans the fice the fpeens antary of eathetint, an thadat hitimes the and cow thig, and
-```
+
+````
 
 These outputs capture the **chaotic creativity** of a character-level model: a mixture of readable words, invented forms, and surprising sequences.
 
@@ -46,9 +49,9 @@ These outputs capture the **chaotic creativity** of a character-level model: a m
 
 ## Intended Uses
 
-* **Character-level text generation experiments**
-* **Research and education**: studying lightweight language models, sequence learning, and text modeling
-* **Creative exploration**: generating quirky text or procedural content for games, demos, or artistic projects
+* **Character-level text generation experiments**  
+* **Research and education:** studying lightweight language models and sequence learning  
+* **Creative exploration:** generating quirky text or procedural content for games, demos, or artistic projects  
 
 > ⚠️ i3-tiny is experimental and **not intended for production or high-stakes applications**. Text may be repetitive, nonsensical, or inconsistent.
 
@@ -56,14 +59,38 @@ These outputs capture the **chaotic creativity** of a character-level model: a m
 
 ## Limitations
 
-* Small vocabulary and character-level modeling limit natural language fluency
-* Outputs are **highly experimental** and not fact-checked
-* Generated sequences can be repetitive or unexpectedly garbled
-* Not aligned or safety-checked
+* Small vocabulary and character-level modeling limit natural language fluency  
+* Outputs are **highly experimental** and not fact-checked  
+* Generated sequences can be repetitive, garbled, or unpredictable  
+* Not aligned or safety-checked  
 
 ---
 
 ## Model Weights
 
-* Stored in `model.bin`
-* Compatible with PyTorch
+* Stored in `pytorch_model.bin` (or `model.safetensors`)  
+* Compatible with PyTorch and Hugging Face Transformers  
+* Requires `modeling_i3.py` and `config.json` to instantiate  
+
+---
+
+## Usage Example
+
+```python
+from modeling_i3 import i3, i3Config
+import torch
+
+config = i3Config.from_pretrained("i3-hf-model")
+model = i3.from_pretrained("i3-hf-model", config=config)
+
+prompt = "Hello"
+input_ids = torch.tensor([[c for c in range(len(prompt))]])  # replace with your dataset encoding
+generated_ids = model.model.generate(input_ids, max_new_tokens=100, temperature=0.8, top_k=20)
+print(generated_ids)  # decode using your dataset method
+````
+
+---
+
+## Citation
+
+If you use i3-tiny for research or experimentation, please cite this repository and acknowledge it as an experimental character-level model.