Workflows/Build a local AI agent with LFM2.5-24B-A2B

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Build a local AI agent with LFM2.5-24B-A2B

Run LFM2.5-24B-A2B with llama.cpp, connect Hermes Agent using Liquid AI’s published settings, and have it read two files and write a checked review.

Verified sourceSource checked 11/9/2026

LOCAL

Freshness 88 out of 100. Release age: 32 days old, Oct 13, 2026. Measures age, not quality.

Popularity 63 out of 100. 35,825 downloads in 30 days; 613 lifetime likes. Checked Nov 13, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source

Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Nov 14, 2026. Statistics checked Nov 13, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

Before you begin

Hardware

Estimated from Liquid AI’s published file sizes plus runtime and context headroom. Only about 2.45B parameters are active per token, so partial offload to system RAM stays usable but slower. Not measured by YouRunAI.

Machine	LFM2.5-24B-A2B · Q4_K_M GGUF	LFM2.5-24B-A2B · Q8_0 GGUF
NVIDIA · 16 GB VRAM + 32 GB RAM	Runs with some layers offloaded to RAM; slower but usable	Heavy offload; choose Q4_K_M instead
NVIDIA RTX 4090 · 24 GB VRAM	Recommended start; fits on the GPU with the starting context	Does not fit fully; offload to RAM or use Q4_K_M
NVIDIA RTX 5090 · 32 GB VRAM	Comfortable, with room for longer context	Fits at the starting context; check free VRAM
AMD Radeon · 24 GB VRAM	Fits; requires the HIP or Vulkan llama.cpp build on Windows (Linux tab here is NVIDIA only)	Partial offload; requires the HIP or Vulkan build
Intel Arc · 16 GB VRAM + 32 GB RAM	Partial offload through the Vulkan llama.cpp build; slower	Heavy offload; choose Q4_K_M instead
Apple Silicon · 24 GB unified memory	Tight: close other apps and keep the starting context	Cannot fit; use Q4_K_M or a larger Mac
Apple Silicon · 32 GB unified memory	Recommended Mac start	Cannot fit in the default GPU memory budget; use Q4_K_M
Apple Silicon · 64 GB unified memory	Comfortable	Comfortable at the starting context
CPU only · 32 GB RAM	Runs on CPU; slow but usable for short tasks	Needs 48 GB RAM or more; use Q4_K_M

Both packages are Liquid AI’s own GGUF files. Liquid AI also publishes a 13.6 GB MLX 4-bit build for Apple Silicon; this guide uses llama.cpp on every platform so the same server works for chat, Cline and Hermes.

LOCAL AGENT · HERMESSOURCE-LINKED SETUP

Build a local AI agent with LFM2.5-24B-A2B

Serve Liquid AI’s GGUF in llama.cpp, point Hermes at it with the publisher’s own configuration, and have the agent read two project files and write review.md. You approve each file action.

Choose a package Run its commands Check the result

BEFORE YOU BEGIN

Choose your package

Select the weights you want. The instructions, downloads and settings change with your choice.

The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.

CURRENT SETUP

LFM2.5-24B-A2B · Q4_K_M GGUF · official

Start at 65,536 tokens for this Hermes agent setup. The 14.5 GB file is only the weights; the runtime and context add more memory. Longer context needs more memory, so reduce it first if the server runs out. Estimate, not a YouRunAI measurement.

Freshness 88 out of 100. Release age: 32 days old, Oct 13, 2026. Measures age, not quality.

Popularity 63 out of 100. 35,825 downloads in 30 days; 613 lifetime likes. Checked Nov 13, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

Check the date source

Popularity · adoption

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Nov 14, 2026. Statistics checked Nov 13, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

View weight source

THE WORKFLOW

Set up LFM2.5-24B-A2B · Q4_K_M GGUF on your machine

Choose your computer first, then its GPU path. The instructions below update to match your selection.

CHOOSE YOUR SETUPWindows

Install llama.cpp with winget

Open PowerShell and install the official llama.cpp release with winget, then close and reopen PowerShell so llama-server is on your PATH. The second command lists the compute devices llama.cpp can use. If only the CPU appears on an NVIDIA or AMD PC, download the CUDA or HIP Windows build from the official releases page instead, extract it, and run the same commands from that folder.

Official llama.cpp releases (Windows builds) Liquid AI GGUF package and run snippets

PowerShell

winget install llama.cpp
llama-server --list-devices

Download Q4_K_M and start the local server

In PowerShell, run this command and leave the window open. The first run downloads the 14.5 GB Q4_K_M file from Liquid AI's GGUF repository; later runs reuse it. The server listens only on this computer at http://127.0.0.1:8080, names the model lfm2.5-24b, and starts with a 65,536-token context and Liquid AI's recommended sampling (temperature 0.1, top_k 50, repetition penalty 1.05). Wait until the log says the server is listening.

Exact Q4_K_M file in the publisher repository llama.cpp server options

PowerShell

llama-server -hf LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M --alias lfm2.5-24b --jinja -c 65536 --temp 0.1 --top-k 50 --repeat-penalty 1.05 --host 127.0.0.1 --port 8080

Install Hermes Agent

Open a second PowerShell window and run the official Hermes installer. Close and reopen PowerShell afterwards so the hermes command is available. This native installer does not need WSL.

Official Hermes installation

PowerShell

iex (irm https://hermes-agent.nousresearch.com/install.ps1)

Point Hermes at the local model

These are the custom-provider settings published on Liquid AI's model card, with the server's lfm2.5-24b alias as the model name. Run them in the second PowerShell window. Hermes then sends every request to llama.cpp on this computer.

Liquid AI Hermes configuration Hermes custom endpoints

PowerShell

hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default lfm2.5-24b

Create two small project files

In the same PowerShell window, create a disposable folder with a launch note and a checklist. The checklist deliberately has no database backup before the migration; the agent should find that gap from the files.

PowerShell

New-Item -ItemType Directory -Force "$HOME\agent-review" | Out-Null
Set-Location "$HOME\agent-review"
@"
Launch day: Thursday 14 May
Owner: Priya
Database migration runs at 09:00 before the app deploy.
"@ | Set-Content notes.md
@"
1. Freeze merges
2. Run the database migration
3. Deploy the app
4. Announce the release
"@ | Set-Content checklist.md

Give Hermes a file-based task

From inside the agent-review folder, start Hermes and paste the task below. Hermes asks before running commands or writing files; read each request before approving it. Approve the file reads and the single write of review.md.

Setting	Value
Task to paste into Hermes	Read notes.md and checklist.md in this folder. Write review.md containing: the launch day and owner, a corrected numbered checklist that adds a verified database backup before the migration, and one sentence explaining the risk of migrating without a backup. Cite the file each fact came from. Do not change notes.md or checklist.md.

Hermes approvals and security

PowerShell

hermes

Inspect the saved review

Open review.md. It should name Thursday 14 May and Priya from notes.md, add a backup step before “Run the database migration”, and explain the risk. Check that Hermes’ activity shows real file reads and one write; an answer only in the chat window is not a completed agent run. Type /exit to leave Hermes and press Ctrl+C in the server window when you are done.

SUCCESS CHECK · LFM2.5-24B-A2B · Q4_K_M GGUF

An agent-written review you can check

review.md names the launch day and owner from notes.md, adds a verified backup before the migration, and Hermes’ activity shows the file reads and one approved write.

Liquid AI lists Hermes as a supported harness and publishes the configuration used here. YouRunAI has not run this exact task with this package; inspect the output rather than trusting the chat summary.

Back to setup steps

When it doesn’t go to plan

Hermes connects but never reads the files.

Check that the server was started with --jinja, the model name is lfm2.5-24b, and you started Hermes inside the agent-review folder. A chat-only answer is not a passing run.

Hermes warns that the context is too small.

Restart the server with a larger -c value if your memory allows, then restart Hermes. Keep the task to these two small files.

The installer finishes but hermes is not found.

Close and reopen the terminal. Follow any PATH or dependency message the installer printed.

REFERENCE LIBRARY

Sources and files

Original instructions, model files, and compatibility notes behind this setup.

6 SOURCES

01Liquid AI model card, license and recommended settingshuggingface.co 02Official LFM2.5-24B-A2B GGUF fileshuggingface.co 03llama.cpp releases and servergithub.com 04LFM Open License v1.0huggingface.co 05Hermes installationhermes-agent.nousresearch.com 06Hermes custom endpointshermes-agent.nousresearch.com

The model behind this workflow

LFM2.5-24B-A2B

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Text

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Verified source