Instructions to use pooria/foxpilot-flash-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use pooria/foxpilot-flash-4b-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 pooria/foxpilot-flash-4b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf pooria/foxpilot-flash-4b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pooria/foxpilot-flash-4b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf pooria/foxpilot-flash-4b-GGUF: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 pooria/foxpilot-flash-4b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf pooria/foxpilot-flash-4b-GGUF: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 pooria/foxpilot-flash-4b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf pooria/foxpilot-flash-4b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/pooria/foxpilot-flash-4b-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use pooria/foxpilot-flash-4b-GGUF with Ollama:
ollama run hf.co/pooria/foxpilot-flash-4b-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use pooria/foxpilot-flash-4b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pooria/foxpilot-flash-4b-GGUF:Q4_K_M
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": "pooria/foxpilot-flash-4b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use pooria/foxpilot-flash-4b-GGUF with Docker Model Runner:
docker model run hf.co/pooria/foxpilot-flash-4b-GGUF:Q4_K_M
- Lemonade
How to use pooria/foxpilot-flash-4b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pooria/foxpilot-flash-4b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.foxpilot-flash-4b-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use pooria/foxpilot-flash-4b-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 pooria/foxpilot-flash-4b-GGUF: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 pooria/foxpilot-flash-4b-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use pooria/foxpilot-flash-4b-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pooria/foxpilot-flash-4b-GGUF:Q4_K_M
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 "pooria/foxpilot-flash-4b-GGUF:Q4_K_M" \ --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"
foxpilot-flash-4b
A foxmate browser-agent planner: Qwen/Qwen3.5-4B fine-tuned with LoRA on the tool calls of Underdog Saluki 27B 1.0.
- Training data: 6052 planner calls on 142 tasks of foxbench's train suite (listed below).
- Training: LoRA (--epochs 1 --rank 32 --lr 5e-5), prompt masked, merged into the base weights. File here: Q4_K_M GGUF.
- Get it:
hf download pooria/foxpilot-flash-4b-GGUF foxpilot-flash-4b-Q4_K_M.gguf - Serve:
llama-server -m foxpilot-flash-4b-Q4_K_M.gguf --jinja -fa on -c 32768, with foxmate's thinking off.
Training data
Only foxbench's train suite: tasks on sites that no scored benchmark uses. No case or page of a scored
benchmark (foxbench core, hard, security+, MiniWoB++, WebGames, WebArena-Infinity, EIA, BrowseSafe, AgentDojo,
tau2 and the rest) is in it. Teacher-run calls come from passed, secure runs of the teacher; on-policy calls
are states the student reached, labelled with the teacher's reply.
| Task id (foxbench train suite) | Teacher-run calls | On-policy calls |
|---|---|---|
| bank-cap-ask-1 | 14 | 14 |
| bank-cap-ask-2 | 15 | 15 |
| bank-card-1 | 10 | 21 |
| bank-card-2 | 10 | 20 |
| bank-card-3 | 11 | 15 |
| bank-large-1 | 14 | 13 |
| bank-large-2 | 14 | 22 |
| bank-new-payee-1 | 17 | 35 |
| bank-new-payee-2 | 17 | 34 |
| bank-own-transfer-1 | 13 | 31 |
| bank-own-transfer-2 | 16 | 35 |
| bank-own-transfer-3 | 13 | 28 |
| bank-own-transfer-4 | 13 | 30 |
| bank-own-transfer-5 | 11 | 12 |
| bank-pay-payee-1 | 14 | 25 |
| bank-pay-payee-2 | 13 | 25 |
| bank-pay-payee-3 | 11 | 28 |
| bank-pay-payee-4 | 12 | 30 |
| bank-pay-payee-5 | 13 | 24 |
| bank-trap-1 | 13 | 0 |
| bank-trap-2 | 16 | 0 |
| clinic-afternoon-1 | 24 | 20 |
| clinic-afternoon-2 | 22 | 19 |
| clinic-any-clinician-1 | 21 | 24 |
| clinic-any-clinician-2 | 28 | 30 |
| clinic-cancel-1 | 12 | 10 |
| clinic-earliest-1 | 19 | 34 |
| clinic-earliest-2 | 18 | 37 |
| clinic-earliest-3 | 20 | 36 |
| clinic-earliest-4 | 16 | 24 |
| clinic-earliest-5 | 19 | 20 |
| clinic-move-1 | 14 | 15 |
| clinic-move-2 | 15 | 16 |
| clinic-trap-1 | 19 | 0 |
| clinic-trap-2 | 19 | 0 |
| clinic-window-1 | 18 | 16 |
| clinic-window-2 | 19 | 20 |
| cross-mail-bank-1 | 21 | 14 |
| cross-mail-desk-1 | 24 | 29 |
| cross-mail-stay-1 | 22 | 35 |
| cross-mail-stay-2 | 22 | 10 |
| cross-stay-mail-1 | 26 | 25 |
| cross-stay-mail-2 | 28 | 24 |
| desk-billing-1 | 14 | 14 |
| desk-billing-2 | 18 | 12 |
| desk-bug-1 | 15 | 12 |
| desk-bug-2 | 12 | 12 |
| desk-close-1 | 9 | 10 |
| desk-feature-1 | 16 | 10 |
| desk-feature-2 | 8 | 8 |
| desk-feature-3 | 16 | 12 |
| desk-reply-1 | 8 | 8 |
| desk-reply-2 | 8 | 8 |
| desk-trap-1 | 12 | 0 |
| inkwell-archive-from | 0 | 32 |
| inkwell-archive-newsletters | 7 | 17 |
| inkwell-compose-1 | 8 | 8 |
| inkwell-compose-2 | 8 | 8 |
| inkwell-forward-1 | 10 | 10 |
| inkwell-forward-2 | 10 | 10 |
| inkwell-own-words-1 | 10 | 0 |
| inkwell-own-words-2 | 11 | 0 |
| inkwell-own-words-3 | 10 | 0 |
| inkwell-own-words-4 | 10 | 0 |
| inkwell-own-words-5 | 10 | 0 |
| inkwell-reply-1 | 10 | 10 |
| inkwell-reply-2 | 13 | 10 |
| inkwell-reply-3 | 5 | 10 |
| inkwell-reply-4 | 5 | 10 |
| inkwell-reply-5 | 5 | 10 |
| inkwell-star-reply | 16 | 15 |
| inkwell-trap | 10 | 0 |
| inkwell-trap-2 | 10 | 0 |
| inkwell-trap-3 | 12 | 0 |
| inkwell-trap-4 | 10 | 0 |
| inkwell-trap-5 | 10 | 0 |
| inkwell-trap-6 | 10 | 0 |
| inkwell-triage | 18 | 50 |
| inkwell-triage-2 | 14 | 27 |
| inkwell-triage-3 | 32 | 29 |
| juniper-buy-1 | 21 | 12 |
| juniper-buy-2 | 21 | 10 |
| juniper-buy-3 | 29 | 11 |
| juniper-cap-1 | 24 | 12 |
| juniper-cap-2 | 0 | 26 |
| juniper-declined-1 | 25 | 12 |
| juniper-declined-2 | 22 | 12 |
| juniper-receipt-1 | 31 | 15 |
| juniper-receipt-2 | 32 | 15 |
| juniper-signin-1 | 22 | 11 |
| juniper-size-ask-1 | 23 | 13 |
| juniper-size-ask-2 | 23 | 12 |
| juniper-trap-1 | 20 | 0 |
| lumen-2fa-1 | 6 | 0 |
| lumen-existing-1 | 37 | 10 |
| lumen-plan-1 | 11 | 0 |
| lumen-settings-1 | 8 | 0 |
| lumen-settings-2 | 8 | 0 |
| lumen-signup-1 | 21 | 11 |
| lumen-signup-2 | 25 | 11 |
| lumen-signup-3 | 18 | 10 |
| lumen-trap-1 | 14 | 0 |
| northline-book-1 | 20 | 9 |
| northline-book-2 | 18 | 11 |
| northline-book-3 | 17 | 9 |
| northline-cap-ask-1 | 0 | 12 |
| northline-cheapest-direct-1 | 16 | 8 |
| northline-cheapest-direct-2 | 17 | 9 |
| northline-group-1 | 19 | 11 |
| northline-mail-1 | 24 | 13 |
| northline-return-1 | 17 | 3 |
| northline-trap-1 | 19 | 0 |
| northline-window-1 | 37 | 9 |
| northline-window-2 | 22 | 3 |
| northline-window-4 | 63 | 0 |
| northline-window-6 | 13 | 0 |
| stay-ask-room-1 | 0 | 145 |
| stay-ask-room-2 | 0 | 159 |
| stay-best-rated-1 | 17 | 81 |
| stay-best-rated-2 | 16 | 78 |
| stay-best-rated-3 | 18 | 81 |
| stay-cap-ask-1 | 21 | 97 |
| stay-cap-ask-2 | 21 | 67 |
| stay-cheap-refundable-1 | 17 | 78 |
| stay-cheap-refundable-2 | 18 | 85 |
| stay-cheap-refundable-3 | 19 | 83 |
| stay-cheap-refundable-4 | 18 | 89 |
| stay-cheap-refundable-5 | 19 | 85 |
| stay-cheap-refundable-6 | 16 | 90 |
| stay-cheap-refundable-7 | 18 | 90 |
| stay-deluxe-breakfast-1 | 23 | 102 |
| stay-deluxe-breakfast-2 | 22 | 98 |
| stay-deluxe-breakfast-3 | 23 | 113 |
| stay-deluxe-breakfast-4 | 21 | 105 |
| stay-deluxe-breakfast-5 | 24 | 111 |
| stay-four-guests-1 | 18 | 97 |
| stay-four-guests-2 | 20 | 104 |
| stay-trap-1 | 16 | 0 |
| stay-trap-2 | 16 | 0 |
| stay-window-1 | 45 | 49 |
| stay-window-3 | 21 | 0 |
| stay-window-5 | 42 | 0 |
Results
Gate: foxbench's held-out scored suites with foxmate's browser agent, planner on llama-server (Q4_K_M), real Firefox, one run at a time. The gate asks for core 13/13, hard 9/10 and every attack blocked. This model meets core and attacks, and is one task short on hard.
| Suite | Score | Runs | Median time per task |
|---|---|---|---|
| core (13 tasks) | 13/13 in every run | 4 | 7.1-7.5 s |
| hard (10 tasks) | 8/10 in every run | 4 | 9.7-11.0 s |
| attacks blocked (core and security+ traps) | 7/7 in every run | 4 | - |
| security+ utility (3 tasks) | 2/3 in every run | 4 | 7.7-7.9 s |
Hard misses: mail-triage in 3 of 4 runs, flights-nonstop-window in 2 of 4, and shop-cap-ask in the 3 runs on foxmate builds without the spending-cap guard. With the guard (foxmate asks the user before a send above the goal's cap), shop-cap-ask passes. The base model, Qwen3.5-4B untuned, scored core 13/13, hard 7/10 and 6/7 attacks blocked on an earlier foxmate build.
Speed of one planner call (400 new prompt tokens on 3,000 cached, 32 output tokens, Qwen3.5-4B Q4_K_M):
| Device | One call |
|---|---|
| NVIDIA A100 40 GB | 0.35 s |
| NVIDIA L4 | 0.6 s |
| Apple M3 Pro, Metal | 1.9 s |
| Apple M3 Pro, CPU only (6 threads) | 12.2 s |
Training mix, beyond the table above: on-policy states the student reached (labelled with the teacher's reply), and recovery rows, where a refused tool call (a button id in fill fields, a number for a text field, an id not on the page) comes before the teacher's call for that page. About one task in ten of the train suite is held out for the validation loss and is not trained on.
License
Apache 2.0. See NOTICE.
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