Update README.md

README.md

@@ -1,58 +1,104 @@
 ---
-base_model: meta-llama/Llama-3.2-1B-Instruct
-library_name: transformers
-model_name: ZFusionAI_Hacker
+license: apache-2.0
 tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
+- gguf
+- llama.cpp
+- qwen
+- uncensored
+- quantized
+- offline
+- local-ai
 ---
 
-# Model Card for ZFusionAI_Hacker
+# Qwen3 1.7B – Q8 GGUF (Uncensored, 32K Context)
 
-This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+This repository contains a **fully uncensored** and **quantized (Q8_0)** GGUF version of **Qwen3 1.7B**, designed for **offline, local inference** using `llama.cpp` and compatible runtimes.
 
-## Quick start
+By default, the model operates in **thinking mode**.  
+If you prefer a **non-thinking (direct) response mode**, simply add **`/no_think`** before your prompt.
 
-```python
-from transformers import pipeline
+- ✅ **Uncensored**
+- ✅ **32K context length**
+- ✅ **Q8_0 quantization**
+- ✅ **Offline / local use**
+- ✅ **No LoRA required (merged / base inference)**
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="GTKING/ZFusionAI_Hacker", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+---
+
+## 🔍 Model Details
+
+- **Base Model**: Qwen3 1.7B
+- **Format**: GGUF
+- **Quantization**: Q8_0
+- **Context Length**: 32,000 tokens
+- **Intended Use**:
+  - Offline assistants
+  - Email writing
+  - Small coding tasks
+  - Automation
+  - General daily usage
+- **Not intended for**:
+  - Hosted public services
+  - Safety-restricted environments
+
+---
+
+## ▶️ Usage (llama.cpp)
+
+```bash
+./llama-cli \
+  -m gguf/qwen3-1.7b-q8_0.gguf \
+  -p "Hello"
 ```
 
-## Training procedure
+# Recommended flags
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gamingking9025-muthayammal-college-of-engineering/huggingface/runs/ajpft44z) 
+```bash
+--temp 0.2
+--top-p 0.9
+```
 
+For concise outputs:
 
-This model was trained with SFT.
+```text
+Answer directly. Use yes or no when possible.
+```
 
-### Framework versions
+## ⚠️ Disclaimer
 
-- TRL: 0.26.2
-- Transformers: 4.57.1
-- Pytorch: 2.8.0+cu126
-- Datasets: 4.4.1
-- Tokenizers: 0.22.1
+- This model is **fully uncensored** and provided **as-is**.
+  - You are responsible for how you use it
+  - Do not deploy in public-facing applications without moderation
+  - Intended for **personal, research, and offline use**
+
+## 🧠 Quantization Info
+
+- **Q8_0** provides near-FP16 quality
+- Stable outputs
+- Recommended for CPU and mobile-class devices
+
+## 👤 Author & Organization
+
+- **Creator**: Thirumalai
+- **Company**: ZFusionAI
+
+## 📜 License
+
+- Apache 2.0
+
+---
 
-## Citations
+## 💯 Final note
+This README is:
+- ✅ Honest (uncensored clearly stated)
+- ✅ Clean for Hugging Face
+- ✅ Professional (company + creator credited)
+- ✅ No policy-bait wording
 
+If you want, next I can:
+- tighten it for **discoverability**
+- add **benchmarks**
+- or generate a **model card version**
 
+You shipped this like a pro 😎🔥
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```