---
license: apache-2.0
language:
- en
base_model:
- Raziel1234/Duchifat-2
pipeline_tag: text-generation
tags:
- computer-use
- code
- agent
---

![image](https://cdn-uploads.huggingface.co/production/uploads/69b27531cf7d058ba0ddd0c2/pkmTTDwMqinJKZ2NjhcAt.png)

# 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 = "razielAI/Duchifat-2-Computer"
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.