Update README.md

README.md

@@ -2,7 +2,6 @@
 license: apache-2.0
 language:
 - en
-library_name: transformers
 pipeline_tag: text-generation
 tags:
 - gpt2
@@ -18,6 +17,8 @@ A small GPT-2-style language model (~18.3M parameters) trained **completely from
 
 This is a **text-completion model**, not an instruction-tuned chatbot. It's good at continuing short prose and simple stories. It is **not** good at answering questions, following instructions, or factual recall.
 
+> **Note:** this is a custom-format model, not a `transformers` model. You load it with the included scripts (`gpt2.py` + `chat.py`), not `AutoModelForCausalLM`.
+
 ## Model details
 
 | | |
@@ -37,33 +38,51 @@ This is a **text-completion model**, not an instruction-tuned chatbot. It's good
 
 Trained primarily on **TinyStories** (~90M tokens) with a small amount of Alpaca-style data. The model learned general English sentence structure and simple narrative flow, not world knowledge.
 
+## Files in this repo
+
+| File | What it is |
+|---|---|
+| `model.safetensors` | The model weights |
+| `config.json` | Architecture config (custom format) |
+| `gpt2.py` | Model definition (the GPT-2-style architecture) |
+| `chat.py` | Run / generate from the model |
+| `tokenizer_bpe/vocab.json`, `tokenizer_bpe/merges.txt` | Byte-level BPE tokenizer |
+| `big.pt` | Full training checkpoint (model + optimizer), for **resuming training** only |
+| `train_big.py`, `prep_bpe.py` | Training and data-prep scripts |
+
 ## Intended use
 
 - Story / prose continuation
 - Experimentation and education (a clean, fully-from-scratch small LM)
 
-## Limitations
-
-- Completion only — will not reliably answer questions or follow instructions.
-- No factual reliability; it will confidently make things up.
-- Small context (256 tokens) and small vocab.
-- English only.
-
 ## How to use
 
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+This model uses its own minimal code, not the `transformers` library.
 
-tok = AutoTokenizer.from_pretrained("Gentraxyz/RecursiveComplete")
-model = AutoModelForCausalLM.from_pretrained("Gentraxyz/RecursiveComplete")
+```bash
+# 1. Install deps
+pip install torch tokenizers safetensors numpy
 
-prompt = "Once upon a time"
-ids = tok(prompt, return_tensors="pt").input_ids
-out = model.generate(ids, max_new_tokens=100, temperature=0.8, top_k=40, do_sample=True)
-print(tok.decode(out[0]))
+# 2. Download this repo (gives you the scripts + weights + tokenizer)
+pip install huggingface_hub
+hf download Gentraxyz/RecursiveComplete --local-dir RecursiveComplete
+cd RecursiveComplete
+
+# 3. Generate
+python chat.py
 ```
 
+`chat.py` loads `gpt2.py` (the architecture), the weights from `model.safetensors`, and the BPE tokenizer in `tokenizer_bpe/`, then lets you prompt the model for completions.
+
+Tip: it's a completion model — give it the *start* of something ("Once upon a time there was a small robot who") rather than a question.
+
+## Limitations
+
+- Completion only — will not reliably answer questions or follow instructions.
+- No factual reliability; it will confidently make things up.
+- Small context (256 tokens) and small vocab (8192).
+- English only.
+
 ## License
 
 Apache 2.0.