Update README.md

README.md

@@ -1,3 +1,114 @@
 ---
+language:
+- en
 license: apache-2.0
+tags:
+- gpt2
+- causal-lm
+- text-generation
+- from-scratch
+- fineweb
+- undertrained
+library_name: transformers
+pipeline_tag: text-generation
 ---
+
+# Llara
+
+<img src="data:image/svg+xml;base64,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">
+
+## Introduction
+
+
+Llara1.1 is a 124M parameter (33M params more than llara1.0) autoregressive language model trained from scratch on English web text. It follows the GPT-2 Small architecture and is trained entirely from random initialisation — no pretrained weights, no distillation, no fine-tuning of an existing model.
+but it does use GPT's tokenizer (sorta)
+
+The name **Llara** is original and unrelated to LLaMA or LoRA.
+
+**Note**: The model is stil undertrained according to `The Chinchilla Laws (2022)`
+
+---
+
+## Improvements 
+ 
+* Incressed context length to 512 tokens
+* Better and clearner training data
+* Able to form cohirent sentences even at 20 max tokens
+* Better GPT config
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Architecture | GPT-2 (decoder-only transformer) |
+| Parameters | ~124.0M |
+| Context length | 512 tokens |
+| Embedding dim | - |
+| Layers | 12 |
+| Attention heads | 12 |
+| Vocabulary | 50,257 (GPT-2 BPE) |
+| Training data | FineWeb (HuggingFaceFW/fineweb) + Custom dataset |
+| Training docs | 131M tokens |
+| Epochs | 1.1 |
+| Precision | fp16 |
+
+---
+
+## Usage
+
+```python
+from transformers import GPT2LMHeadModel, AutoTokenizer, pipeline
+
+model = GPT2LMHeadModel.from_pretrained("helloadhavan/llara1.1-100M-base")
+tokenizer = AutoTokenizer.from_pretrained("helloadhavan/llara1.1-100M-base")
+
+gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+output = gen(
+    "Once upon a time",
+    max_new_tokens=20,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.95,
+    repetition_penalty=1.1,
+)
+
+print(output[0]["generated_text"])
+```
+
+---
+
+## Limitations
+
+- Llara is trained on English web text only and performs poorly on other languages.
+- Like all autoregressive LMs trained on web data, it may reproduce biases, factual errors, or inappropriate content present in the training corpus.
+- It is a research model trained from scratch and is not instruction-tuned or aligned — it should not be used in production or user-facing applications without further fine-tuning and safety work.
+- At 124M parameters and 2M training documents, it is significantly smaller and less trained than models like GPT-2 (which saw 40GB of text). Outputs may be incoherent on complex prompts.
+
+---
+
+## Intended Use
+
+Llara is intended for:
+
+- Research and experimentation with small language models
+- Learning how GPT-style models are trained from scratch
+- A base for fine-tuning on downstream tasks
+
+---
+
+## Training Framework
+
+Trained using [Hugging Face Transformers](https://github.com/huggingface/transformers) `Trainer` on a single GPU.
+
+---
+
+## License
+
+Apache 2.0
+
+<div>
+  <blockquote><strong>Note:</strong> i am a AI hobbyist, not an AI engineer</blockquote>
+</div>