Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Raziel1234/Duchifat-2
+pipeline_tag: text-generation
+tags:
+- computer-use
+- code
+- agent
+---
+
+# Duchifat-2-Computer-v1 🕊️💻
+
+
+
+## Overview
+**Duchifat-2-Computer-v1** is a high-precision, specialized Small Language Model (SLM) with **136M parameters**. This model is a fine-tuned version of the base `Duchifat-2`, specifically engineered for **Task-Oriented Control** and **CLI Automation**.
+
+Through aggressive Supervised Fine-Tuning (SFT) and "Hard Alignment," we have eliminated general-purpose hallucinations (such as irrelevant PDF/Video references) to create a reliable bridge between natural language instructions and executable computer actions.
+
+## 🤖 The Core Engine of CLI-Assistant
+This model is designed to function as the primary reasoning engine for the **CLI-Assistant** project. It transforms human intent into structured tool-calls with near-zero latency.
+
+🔗 **To see the full implementation and integrate this model into your system, visit:**
+👉 [CLI-Agent on GitHub](https://github.com/nevo398/CLI-Agent)
+
+
+
+## Key Features
+- **Deterministic Alignment:** Optimized for precise tool-calling formats (e.g., `[SAY_TEXT]`, `[CREATE_NOTE]`).
+- **Ultra-Lightweight:** 136M parameters allow for lightning-fast inference on CPU/Edge devices or low-cost API endpoints.
+- **Context-Aware:** Understands complex instructions involving times, dates, and nested technical content.
+- **Zero-Hallucination:** Drastically reduced pre-training bias to ensure the model stays within the "Computer Action" domain.
+
+## 🛠️ Usage & Prompt Template
+To achieve the best results, the model must be prompted using the following format:
+
+```text
+<instruction> {Your Command Here} </instruction>
+<assistant>
+```
+
+## Example
+
+# User input:
+```Say 'The backup is complete'```
+
+# Model Output:
+```[SAY_TEXT]("The backup is complete")```
+
+## Quick Start(Inference)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "Raziel1234/Duchifat-2-Computer-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda")
+
+prompt = "<instruction> Say 'The backup is complete' </instruction>\n<assistant> "
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training Details
+- **Base Model**: Duchifat-2(Pre-trained on 3.27B tokens)
+- **SFT Technique**: High-LR Hard Alignment (1e-4)
+- **Epochs:**  80 (Aggressive Alignment)
+- **Hardware**: Trained on T4 via Google Colab.
+
+## LICENSE
+
+This model is released under the Apache 2.0 License. Please refer to the [CLI-Agent on GitHub](https://github.com/nevo398/CLI-Agent) repository for additional integration guidelines.
+