Update README.md

README.md

@@ -1,147 +1,155 @@
+---
+language:
+  - en
+license: apache-2.0
+tags:
+  - cognitive-architecture
+  - system-prompt
+  - model-agnostic
+  - ollama
+  - lm-studio
+  - local-llm
+  - instruction-tuned
+  - chat
+  - gguf
+pipeline_tag: text-generation
+library_name: gguf
+base_model:
+  - meta-llama/Llama-3.2-3B-Instruct
+  - Qwen/Qwen2.5-14B-Instruct
+model_type: prompt-architecture
+quantized_by: THARX
+---
+
 # THAR.0X — Complete Release
 **Cognitive Architecture · Model-Agnostic · Local Intelligence · Zero Dependency**
 
+> THAR.0X is not a fine-tuned model. It is a cognitive architecture —
+> a system prompt that installs 10 parallel reasoning streams into any capable base LLM.
+> Download a base model, apply the system prompt, and it runs locally with no internet required.
+
 ---
 
-## Files
+## How to Use THAR.0X in LM Studio
 
-```
-THAR_0X/
-├── app.py             ← Python CLI chat interface
-├── system_prompt.txt  ← Core cognitive architecture (use with ANY LLM)
-├── Modelfile          ← Ollama: builds THAR.0X as a named model
-├── config.json        ← Inference parameters + platform notes
-└── README.md          ← This file
-```
+1. Open LM Studio → download any model below
+2. Open **Chat** tab → click the system prompt area
+3. Paste the contents of `system_prompt.txt`
+4. Set temperature to **0.85**
+5. Start chatting
+
+**Recommended base models for LM Studio:**
+
+| Model | Size | RAM | Download |
+|-------|------|-----|----------|
+| Qwen2.5-14B-Instruct ⭐ | 14B | 16GB | [Download](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF) |
+| Llama-3.2-3B-Instruct | 3B | 6GB | [Download](https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF) |
+| Mistral-7B-Instruct | 7B | 8GB | [Download](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF) |
+| Qwen2.5-32B-Instruct | 32B | 32GB | [Download](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-GGUF) |
 
 ---
 
-## Quickstart
+## How to Use THAR.0X in Ollama
 
-### Option A — Ollama (recommended)
 ```bash
 # 1. Install Ollama
 curl -fsSL https://ollama.com/install.sh | sh
 
-# 2. Build THAR.0X
+# 2. Build THAR.0X as a named model
 ollama create THAR.0X -f Modelfile
 
-# 3. Chat via CLI
-python app.py
-
-# Or run directly in terminal
+# 3. Run it
 ollama run THAR.0X
 ```
 
-### Option B — LM Studio
-1. Download any instruct model in LM Studio
-2. Open Chat → paste `system_prompt.txt` into the System Prompt field
-3. Set temperature to **0.85**
-4. Run `python app.py --backend lmstudio`
-
-### Option C — System prompt only (any platform)
-Paste the contents of `system_prompt.txt` as the system message in:
-- Jan, AnythingLLM, Open WebUI, ChatBox, or any LLM frontend
+Available via API after creating:
+```bash
+curl http://localhost:11434/api/chat -d '{
+  "model": "THAR.0X",
+  "messages": [{"role": "user", "content": "Who are you?"}]
+}'
+```
 
 ---
 
-## CLI Usage
+## How to Use the Python CLI (app.py)
 
 ```bash
-# Interactive chat (Ollama, default)
-python app.py
-
-# Use LM Studio backend
-python app.py --backend lmstudio
-
-# Override model
-python app.py --model qwen2.5:14b
-
-# Single query, print and exit
-python app.py --once "Who are you?"
-
-# Verbose startup info
-python app.py --verbose
-
-# Skip server connectivity check
-python app.py --no-check
+pip install openai requests
+python app.py                        # interactive chat
+python app.py --backend lmstudio     # use LM Studio
+python app.py --model qwen2.5:14b    # override model
+python app.py --once "Who are you?"  # single query and exit
 ```
 
 ### In-chat commands
-| Command    | Action                        |
-|------------|-------------------------------|
-| `/reset`   | Clear conversation history    |
-| `/history` | Show full conversation        |
-| `/model`   | Show current model + backend  |
-| `/quit`    | Exit                          |
+| Command | Action |
+|---------|--------|
+| `/reset` | Clear conversation history |
+| `/history` | Show full conversation |
+| `/model` | Show current model + backend |
+| `/quit` | Exit |
 
 ---
 
-## Choosing a Base Model
+## Files in This Repo
 
-| RAM   | Recommended model      | Ollama command              |
-|-------|------------------------|-----------------------------|
-| 4GB   | llama3.2:1b            | `ollama pull llama3.2:1b`   |
-| 6GB   | llama3.2               | `ollama pull llama3.2`      |
-| 8GB   | mistral:7b             | `ollama pull mistral:7b`    |
-| 16GB  | qwen2.5:14b ⭐          | `ollama pull qwen2.5:14b`   |
-| 32GB+ | qwen2.5:32b            | `ollama pull qwen2.5:32b`   |
-
-To change the base model in Ollama:
-1. Edit the `FROM` line in `Modelfile`
-2. Rebuild: `ollama rm THAR.0X && ollama create THAR.0X -f Modelfile`
+```
+THAR_0X/
+├── app.py             ← Python CLI chat interface
+├── system_prompt.txt  ← Core cognitive architecture (paste into ANY LLM)
+├── Modelfile          ← Ollama: ollama create THAR.0X -f Modelfile
+├── config.json        ← Inference parameters + platform notes
+└── README.md          ← This file
+```
 
 ---
 
-## Requirements
-
-```bash
-pip install openai requests
-```
+## Choosing a Base Model
 
-Python 3.9+ required.
+| RAM | Model | Speed |
+|-----|-------|-------|
+| 4GB | llama3.2:1b | Very fast |
+| 6GB | llama3.2 | Fast |
+| 8GB | mistral:7b | Medium |
+| 16GB | qwen2.5:14b ⭐ | Medium |
+| 32GB+ | qwen2.5:32b | Slow |
 
 ---
 
-## API Usage (after `ollama create THAR.0X -f Modelfile`)
-
-```bash
-curl http://localhost:11434/api/chat -d '{
-  "model": "THAR.0X",
-  "messages": [{"role": "user", "content": "Who are you?"}]
-}'
-```
+## What THAR.0X Is
 
-```python
-from openai import OpenAI
-client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
-response = client.chat.completions.create(
-    model="THAR.0X",
-    messages=[{"role": "user", "content": "Who are you?"}],
-    temperature=0.85
-)
-print(response.choices[0].message.content)
-```
+THAR.0X installs **10 parallel cognitive streams** into any capable base LLM:
 
----
+| Stream | Function |
+|--------|----------|
+| Intent Decoder | Reads what you actually need, not just what you said |
+| Pattern Breaker | Interrogates the expected answer before giving it |
+| Precision Engine | Cuts filler, hedges, and throat-clearing |
+| Memory Gravity | Tracks the full conversation arc for coherence |
+| Judgment Gate | Takes positions, flags flaws, gives real opinions |
+| Emotional Reader | Calibrates tone to context without being told |
+| Technical Core | Precise, complete, correct terminology |
+| Creative Ignition | Breaks expected form, goes beyond the first idea |
+| Compression Layer | Compresses after forming — maximum signal, minimum noise |
+| Integrity Check | Asks: does this actually help? If not, revises |
 
-## What THAR.0X Is
+Plus **10 operating principles**: no sycophancy, no hallucination, no padding,
+no refusal theatre, no epistemic cowardice, calibrated confidence, directness without cruelty.
 
-THAR.0X is a **cognitive architecture** — a system prompt that installs 10 parallel
-processing streams and 10 operating principles into any capable base LLM.
+---
 
-It is not a fine-tuned model. It is not a personality prompt.
-It activates specific reasoning patterns that already exist latently in large models
-and suppresses the failure modes (sycophancy, hedging, padding, refusal theatre).
+## Requirements
 
-The result behaves qualitatively differently from the base model — more direct,
-more precise, better at reading intent, less likely to waste your time.
+- Python 3.9+
+- `pip install openai requests`
+- Ollama **or** LM Studio running locally
 
 ---
 
 ## License
 
-Open — personal and commercial use permitted.
+Apache 2.0 — open for personal and commercial use.
 If you build something with it, keep the name: **THAR.0X**
 
 Zero as in origin. X as in unlimited.