Instructions to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Use Docker

docker model run hf.co/Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Llama new
LM Studio
Jan
Ollama
How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with Ollama:
```
ollama run hf.co/Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0
```
Unsloth Desktop

How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with Docker Model Runner:
```
docker model run hf.co/Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0
```

Lemonade

How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Run and chat with the model

lemonade run user.LFM3-1.7B-BrowserAgent-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

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 Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Duckets/LFM3-1.7B-BrowserAgent-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Duckets/LFM3-1.7B-BrowserAgent-GGUF:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LFM3-1.7B-BrowserAgent (GGUF Q8_0)

LiquidAI LFM3-1.7B fine-tuned to operate BrowserOS and BrowserOS neo through their real MCP tool surfaces — snapshot → act → verify loops, batched form filling, tab/session management, error recovery, and the request_human_help discipline for logins, one-time codes, captchas, and money approvals.

Training data

1,553 training rows (+173 held-out eval), fully synthetic, built from the open-source browseros-ai/BrowserOS repo (tool definitions, the repo's own neo agent skill, and docs) and the live tools/list of a running BrowserOS neo (25 tools). No personal information — the dataset was screened before training. Task families: inbox triage, unsubscribing, social posting, CRM updates, expense filing with receipt upload, flight search, PDF archiving, settings changes, history reopen, tab groups, saved skills, parallel multi-tab research, two-task sessions, next-call decisions from page snapshots, error recovery, and human-help cases. Element refs in training snapshots were randomized with distractor elements so the model reads the snapshot instead of memorizing positions.

Held-out eval after 1 epoch: val loss 0.092, token accuracy 97.9%.

Recipe

LoRA (rank 8, alpha 10) on the MLX 8-bit base via mlx-vlm SFT, lr 2e-4, batch 4, max seq 2048, 1 epoch (389 iters). Adapter fused back into the base, converted to GGUF with llama.cpp's LFM3 converter, quantized to Q8_0. An MLX build is kept locally; the GGUF is the LM Studio-loadable artifact today.

Files

lfm3-1.7b-browseragent-604a-Q8_0.gguf — the model.

Notes

Runs in LM Studio (llama.cpp engine) and llama.cpp.
Pairs with BrowserOS neo's MCP server (browseros-neo) or the classic BrowserOS MCP server; the model emits tool calls as JSON blocks naming the tool and its arguments.
Base model: LiquidAI/LFM3-1.7B (see its card for license terms).

Downloads last month: 108

GGUF

Model size

2B params

Architecture

lfm3

Hardware compatibility

8-bit

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