ckacor/my-lora-model

---

license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - lora - qwen2.5 - fine-tuned - function-calling - tool-use - system-administration - gguf - ollama pipeline_tag: text-generation language: - ru - en library_name: transformers

🦅

Zero-latency SysAdmin assistant. Optimized for instant Linux tool chaining via JSON function calling. No hallucinations, just operations.

ckacor@ops:~

$ ollama run ckacor/hawk-1.5b \
  > "Get GPU temp and check system uptime"

Execution Ready

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Technical architecture & capabilities

Hawk-1.5B by ckacor is a specialized agent model built on Qwen2.5-1.5B-Instruct and fine-tuned using the QLoRA methodology.

Its purpose is to parse natural-language requests (EN/RU) and convert them into structured, executable JSON function calls for system administration tasks. It does not produce conversational text — it produces operational data.

Out-of-the-box toolchain

Tool function	Description	Typical use case
get_gpu_status	Returns GPU metrics: VRAM, load, temperature	Inference node monitoring
execute_terminal_command	Runs an arbitrary Bash command	DevOps automation, daemon management
read_file	Reads the contents of a specified file path	Config inspection, log parsing

Example prompts it handles

Prompt	Model output
🇬🇧 "Check GPU utilization"	{"tool": "get_gpu_status", "arguments": {}}
🇬🇧 "Uptime?"	{"tool": "execute_terminal_command", "arguments": {"command": "uptime"}}
🇷🇺 "Проверь дисковое пространство"	{"tool": "execute_terminal_command", "arguments": {"command": "df -h"}}
🇷🇺 "Покажи конфиг config.json"	{"tool": "read_file", "arguments": {"filepath": "config.json"}}

No formal latency/accuracy benchmarks have been measured yet — numbers will be added here once evaluated, rather than estimated.

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Instant deployment with Ollama

1. Download the GGUF from the Files and versions tab.
2. Create a Modelfile:

FROM ./hawk-1.5b-q8_0.gguf
PARAMETER temperature 0.1
PARAMETER top_p 0.95
PARAMETER repeat_penalty 1.1
SYSTEM """
You are HAWK-1.5B,
a Linux automation AI agent developed by ckacor.
Your task is to transform user requests
into accurate function calls.
"""

3. Build and run:

ollama create hawk-1.5b -f Modelfile
ollama run hawk-1.5b

> Check GPU temperature

{
  "tool": "get_gpu_status",
  "arguments": {}
}

Via Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ckacor/my-lora-model")
tokenizer = AutoTokenizer.from_pretrained("ckacor/my-lora-model")

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Files and versions

Only q8_0 is available right now. Additional quantizations will be added once generated.

File	Description
hawk-1.5b-q8_0.gguf	Maximum precision (available)
hawk-1.5b-q5_k_m.gguf	Higher quality, smaller size — coming soon
hawk-1.5b-q4_k_m.gguf	Recommended for most users — coming soon

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Recommended runtime settings

Parameter	Value
Temperature	0.1 – 0.3
Top P	0.9 – 0.95
Context	depends on hardware
Quantization	Q4_K_M recommended once available
Mode	deterministic generation

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Training details

Base model	Qwen2.5-1.5B-Instruct
Method	QLoRA
Data	Custom dataset of tool-calling examples for system administration tasks
Languages	Russian, English

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Roadmap

Model	Focus	Status
Hawk-1.5B	General tool-use / sysadmin	✅ released (this repo)
Hawk-1.5B-Instruct	Broader instruction following	🔜 planned
Hawk-1.5B-Reasoning	Multi-step reasoning	🔜 planned
Hawk-1.5B-Code	Code generation	🔜 planned
Hawk-1.5B-Vision	Image understanding	🔜 planned

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Limitations

• Not designed for long creative writing
• Not a replacement for large general-purpose LLMs
• Tool execution requires an external agent layer — this model only outputs the JSON, it does not execute commands itself
• Performance depends on fine-tuning data quality
• May occasionally switch languages mid-response
• No formal benchmark numbers have been published yet

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Safety notes

HAWK-1.5B only generates commands and actions — it does not execute them. The execution layer should always:

• validate commands before running them
• restrict permissions to the minimum necessary
• use sandboxing where possible
• require confirmation for destructive operations

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About ckacor

ckacor develops lightweight AI systems focused on:

• local artificial intelligence
• machine learning experiments
• Linux automation
• efficient models for limited hardware

ckacor AI
    |
    ▼
 HAWK Family
    |
    ├── Hawk-1.5B
    |
    └── Future Models

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License

This model follows the license specified in the model repository (Apache 2.0). Please check the license before commercial deployment.

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Citation

@misc{ckacor2026hawk,
  title  = {Hawk-1.5B: a LoRA fine-tune of Qwen2.5-1.5B for sysadmin tool-use},
  author = {ckacor},
  year   = {2026},
  url    = {https://huggingface.co/ckacor/my-lora-model}
}

---

🦅 HAWK-1.5B

Built by ckacor

Local AI. Efficient automation.