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
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:
<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)
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 repository for additional integration guidelines.