HydroShell-1.2B / README.md

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metadata

license: apache-2.0
base_model: LiquidAI/LFM2.5-1.2B-Instruct
tags:
  - linux
  - terminal
  - bash
  - devops
  - liquid-foundation-model
  - multilingual
  - arabic
languages:
  - en
  - ar
  - ta
metrics:
  - accuracy
model_name: HydroShell-1.2B
datasets:
  - missvector/linux-commands
language:
  - en
  - ar

HydroShell-1.2B: Liquid Linux Expert

HydroShell-1.2B is a specialized, multilingual fine-tuned version of the Liquid AI (LFM 2.5 1.2B) model. It is optimized to act as a high-performance, low-latency assistant for Linux system administration, shell scripting, and DevOps automation.

By leveraging the Liquid Foundation Model architecture, HydroShell excels at processing long-form technical instructions and mapping complex natural language (English, Arabic, and Tamil) to functional Bash one-liners.

⚠️ Safety & Destructive Command Warning

WARNING: This model is designed to generate powerful system-level commands. It can and will generate destructive commands (e.g., rm -rf, mkfs, or overwriting configurations with >).

Always verify commands in a sandbox or test environment before executing them on production systems.

The model may occasionally hallucinate flags or mix Linux distributions (e.g., suggesting pacman for Ubuntu systems).

Model Details

Developed by: [Your Name/MindLab]
Base Model: LiquidAI/LFM2.5-1.2B-Instruct
Architecture: Liquid Foundation Model (Dynamical Systems-based)
Primary Domain: Linux CLI, Bash Scripting, System Hardening.
Languages Supported: English, Arabic (Technical).

Evaluation Results (Zero-Shot Testing)

The following results were observed during a 100-prompt "Stress Test" covering System Audit, Security, and File Management.

Technical Performance Matrix

Category	Accuracy	Notes
Basic Admin (`ls`, `cd`, `mkdir`)	98%	Flawless execution.
Log Parsing (`awk`, `sed`, `grep`)	75%	Occasionally confuses line vs. field flags.
Systemd & Services	90%	Strong understanding of service lifecycles.
Networking (`iptables`, `ss`)	82%	Occasional source/destination flag inversion.

Multilingual Capability

Arabic: 90% Accuracy in intent recognition. Successfully maps Arabic technical terms like "حظر" (Block) and "مزامنة" (Sync).
English: 95% Accuracy in intent recognition.

Known Issues & Limitations

Distro Confusion: The model may suggest Arch Linux (pacman) commands when asked for Ubuntu tasks if the prompt is not specific.
Redirection Risks: In some tests, the model used > (overwrite) instead of >> (append) for configuration files.
Hallucination: For very complex find commands, it may invent non-existent flags (e.g., -md5).

Usage (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "your-username/HydroShell-1.2B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

messages = [{"role": "user", "content": "البحث عن العمليات التي تستهلك أكبر قدر من الذاكرة"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=64, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

If you use this model in your research or projects, please cite the base Liquid AI model and this fine-tuned version.

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