Update README.md

README.md

@@ -1,38 +1,45 @@
-# ──────────────────────────────
-# Prepare "Dave" Hugging Face-ready repo
-# ──────────────────────────────
+---
+language: en
+license: apache-2.0
+tags:
+  - causal-lm
+  - custom
+  - transformer
+  - from-scratch
+  - conversational-ai
+---
 
-!pip install -q torch transformers tokenizers
+# Dave – Fully Custom AI Model
 
-import os
-import json
-import torch
-import torch.nn as nn
-from tokenizers import Tokenizer, models, pre_tokenizers, trainers, processors
-
-# -----------------------------
-# Settings
-# -----------------------------
-model_name = "dave"
-output_dir = f"./{model_name}"
-os.makedirs(output_dir, exist_ok=True)
-print(f"Using folder: {output_dir}")
-
-# -----------------------------
-# README.md for Hugging Face
-# -----------------------------
-readme_content = """# Dave – Fully Custom AI Model
-
-Dave is a fully custom-built transformer trained from scratch.
-- Personality: fun, smart, coding-savvy, depressed, talks about drugs/alcohol, lost wife, has a kid, sometimes cringe, has close friends, swears frequently.
-- 3000 prompts/responses were used for training.
-- Architecture: custom Transformer implementation.
-- Trained from scratch.
-
-## How to use
+Dave is a fully custom AI model trained completely from scratch.  
+It has a fun, smart, coding-savvy, slightly depressed personality, talks about drugs and alcohol, is sometimes cringe, and has close friends.  
+This model contains **3000 unique prompt/response pairs** created for training Dave’s personality.
+
+## Model Files
+
+The repository contains:
+
+- `pytorch_model.bin` – the trained model weights
+- `config.json` – model configuration
+- `tokenizer.json` – the tokenizer
+- `generation_config.json` – generation settings for sampling outputs
+
+## How to Use
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
+# Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("DSDUDEd/Dave")
 model = AutoModelForCausalLM.from_pretrained("DSDUDEd/Dave")
+
+# Example prompt
+prompt = "Hey Dave, give me coding advice."
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate output
+outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.7, top_p=0.9)
+
+# Decode and print
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))