Text Generation
GGUF
English
function-calling
agent
homelab
gemma
qwen
eightly-os
tool-use
conversational
Instructions to use smashingtags/eightly-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use smashingtags/eightly-agent with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="smashingtags/eightly-agent", filename="eightly-agent-e2b-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smashingtags/eightly-agent with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf smashingtags/eightly-agent:Q4_K_M # Run inference directly in the terminal: llama-cli -hf smashingtags/eightly-agent:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf smashingtags/eightly-agent:Q4_K_M # Run inference directly in the terminal: llama-cli -hf smashingtags/eightly-agent:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf smashingtags/eightly-agent:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf smashingtags/eightly-agent:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf smashingtags/eightly-agent:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf smashingtags/eightly-agent:Q4_K_M
Use Docker
docker model run hf.co/smashingtags/eightly-agent:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use smashingtags/eightly-agent with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smashingtags/eightly-agent" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smashingtags/eightly-agent", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/smashingtags/eightly-agent:Q4_K_M
- Ollama
How to use smashingtags/eightly-agent with Ollama:
ollama run hf.co/smashingtags/eightly-agent:Q4_K_M
- Unsloth Studio
How to use smashingtags/eightly-agent with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for smashingtags/eightly-agent to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for smashingtags/eightly-agent to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for smashingtags/eightly-agent to start chatting
- Pi
How to use smashingtags/eightly-agent with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf smashingtags/eightly-agent:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "smashingtags/eightly-agent:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use smashingtags/eightly-agent with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf smashingtags/eightly-agent:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default smashingtags/eightly-agent:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use smashingtags/eightly-agent with Docker Model Runner:
docker model run hf.co/smashingtags/eightly-agent:Q4_K_M
- Lemonade
How to use smashingtags/eightly-agent with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smashingtags/eightly-agent:Q4_K_M
Run and chat with the model
lemonade run user.eightly-agent-Q4_K_M
List all available models
lemonade list
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language:
- en
license: apache-2.0
tags:
- function-calling
- agent
- homelab
- gemma
- qwen
- eightly-os
- tool-use
- conversational
- gguf
pipeline_tag: text-generation
---
# Eight.ly Agent
Fine-tuned on 4,684 Eight.ly OS tool-calling examples across 41 NAS management tools (Docker, storage, VMs, LXC, file sharing, system administration).
## Architecture
```
user query
|
v
+---------------+
| Nova Router |
| (Go intent |
| classifier) |
+-------+-------+
|
+----+----+
v v
+------+ +----------------+
| fg | | conversational |
|359MB | | tier (gemma2, |
|tool | | q4b, q8b, e2b) |
|calls | | |
+------+ +----------------+
```
The Nova Router is a zero-latency Go pattern matcher (57 test cases) that classifies queries into `no_tool`, `tool`, or `maybe`. Tool queries route to FunctionGemma for structured tool-call extraction via GBNF grammar. Conversational queries skip directly to the response model.
## Evaluation
| Metric | Value |
|--------|-------|
| Tool-call accuracy (fg) | 87.5% (13/15 standard audit suite) |
| FunctionGemma latency | Sub-1s on CPU |
| Median tool-query response | 6.9s end-to-end |
| No-tool response time | 1-3s |
| System queries | 10-18s |
| Container/health queries | 6-14s |
Evaluated by Opus 4.7 auditor over 8 rounds. Remaining sharp edges: Docker stop/kill semantics, acknowledgment over-eagerness, storage pool labeling.
## Models
| Model | Base | GGUF Size | Role |
|-------|------|-----------|------|
| eightly-agent-fg | FunctionGemma 270M | 359 MB | Tool router (dual-model worker) |
| eightly-agent-q4b | Qwen3 4B | 2.4 GB | Single-model fallback |
| eightly-agent-q8b | Qwen3 8B | 4.7 GB | Best single-model quality |
| eightly-agent-e2b | Gemma 4 E2B | 3.2 GB | Experimental (not yet deployed) |
Conversational tier uses stock `gemma2:2b` (1.6 GB) as the response synthesizer.
## Training Data
- 4,684 tool-calling examples (FunctionGemma fine-tune)
- 41 tools across 6 domains: Docker, Storage, VMs, LXC, File Sharing, System
- Dataset: [smashingtags/eightly-agent-dataset](https://huggingface.co/datasets/smashingtags/eightly-agent-dataset)
## Usage
```bash
# Pull the FunctionGemma tool router
ollama pull smashingtags/eightly-agent-fg
# Pull the single-model fallback (Qwen3 4B)
ollama pull smashingtags/eightly-agent-q4b
# Pull the high-quality single-model (Qwen3 8B)
ollama pull smashingtags/eightly-agent-q8b
# Run locally
ollama run smashingtags/eightly-agent-q4b
```
These models are designed to work within the Eight.ly OS Nova assistant pipeline. FunctionGemma expects a scoped tool catalog with GBNF grammar constraints. The q4b/q8b models support native Ollama tool calling.
## Example: Tool-Calling Flow
**User:** "How much disk space is free?"
**Step 1 — FunctionGemma routes to tool:**
```
<start_function_call>call:get_storage_status{}<end_function_call>
```
**Step 2 — Tool executes, returns real data:**
```json
{"mountpoint": "/mnt/storage", "total": "69.8 GB", "used": "22 MB", "free": "69.8 GB", "percent": "0.03%"}
```
**Step 3 — Conversational model responds:**
> "Your storage pool at /mnt/storage has 69.8 GB free out of 69.8 GB total — essentially empty at 0.03% used."
End-to-end: ~6.9 seconds median. FunctionGemma decision: sub-1 second.
## 41 Tools
**Docker:** list_containers, get_container_logs, get_container_stats, list_docker_stacks, list_docker_images, list_docker_networks, list_docker_volumes, container_action, pull_docker_image, install_app
**Storage:** get_storage_status, get_storage_capacity, get_cache_status, get_snapraid_status, get_disk_health, get_zfs_pools, run_snapraid_sync, run_smart_test, spin_down_disks, create_backup
**VMs:** list_vms, get_vm_stats, list_vm_snapshots, vm_action, create_vm_snapshot
**LXC:** list_lxc_containers, lxc_action
**File Sharing:** get_smb_shares, get_nfs_exports, create_smb_share, create_nfs_export
**System:** get_system_info, get_system_version, get_system_logs, get_node_time, get_network_interfaces, get_firewall_rules, get_health_overview, get_nova_models, search_apps, set_timezone, reboot_system
## Roadmap
- Tool catalog growing from 41 tools. Next: scheduling tools, backup management, network diagnostics.
- Multi-turn context for follow-up questions.
- Additional domain scoping refinements based on real user feedback.
- Gemma 4 E2B deployment and evaluation.
## Links
- **Product:** [eight.ly](https://eight.ly)
- **Dataset:** [smashingtags/eightly-agent-dataset](https://huggingface.co/datasets/smashingtags/eightly-agent-dataset)
- **Discord:** [discord.gg/Y9jbyrnTTj](https://discord.gg/Y9jbyrnTTj)
## License
Apache 2.0
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