Instructions to use pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use pablitoelpeligro/aibudget-intent-05b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pablitoelpeligro/aibudget-intent-05b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pablitoelpeligro/aibudget-intent-05b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M

Ollama
How to use pablitoelpeligro/aibudget-intent-05b-GGUF with Ollama:
```
ollama run hf.co/pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use pablitoelpeligro/aibudget-intent-05b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pablitoelpeligro/aibudget-intent-05b-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": "pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pablitoelpeligro/aibudget-intent-05b-GGUF with Docker Model Runner:
```
docker model run hf.co/pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M
```

Lemonade

How to use pablitoelpeligro/aibudget-intent-05b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.aibudget-intent-05b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-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 pablitoelpeligro/aibudget-intent-05b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pablitoelpeligro/aibudget-intent-05b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pablitoelpeligro/aibudget-intent-05b-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 "pablitoelpeligro/aibudget-intent-05b-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"

pablitoelpeligro commited on 1 day ago

Commit

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Update README.md

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@@ -22,7 +22,7 @@ language:
 pipeline_tag: text-generation
 ---
-Fine-tuned Qwen2.5-0.5B GGUF for on-device personal finance chat. Maps questions to strict JSON intents (spend, merchants, categories, budgets…) for local SQLite tools. EN/IT/ES/FR. ~398 MB Q4_K_M.
 # AiBudget Intent 0.5B v5
@@ -31,6 +31,8 @@ Fine-tuned Qwen2.5-0.5B GGUF for on-device personal finance chat. Maps questions
 > Maps natural-language questions about personal spending (EN / IT / ES / FR) into a single, schema-constrained JSON intent object.
 > The AiBudget mobile app parses the JSON and runs **local SQLite** tools. When users choose this on-device model, **transaction data is not sent to a cloud LLM for intent parsing**.
 **Published artifact:** fused, quantized **GGUF only** — LoRA adapter weights are **not** in this repository.
 **Recommended download:** [`aibudget-intent-05b-v5-Q4_K_M.gguf`](https://huggingface.co/pablitoelpeligro/aibudget-intent-05b-GGUF/resolve/main/aibudget-intent-05b-v5-Q4_K_M.gguf) (~398 MB, SHA256 `d3d6fc0780359cd4d198e9354b9ecfc0ff9cfb0fb729f040326fa6e2f02c693a`)

 pipeline_tag: text-generation
 ---
+LoRA fine-tune of Qwen2.5 **0.5B** (~398 MB Q4_K_M) for phones: small enough to download over cellular and run on-device, specialized for personal-finance intent JSON (EN/IT/ES/FR).
 # AiBudget Intent 0.5B v5
 > Maps natural-language questions about personal spending (EN / IT / ES / FR) into a single, schema-constrained JSON intent object.
 > The AiBudget mobile app parses the JSON and runs **local SQLite** tools. When users choose this on-device model, **transaction data is not sent to a cloud LLM for intent parsing**.
+**Why a 0.5B model?** We deliberately started from a **very small** instruct model so the shipped artifact stays around **one download (~400 MB)** and can run **locally on mid-range phones** without a cloud API. LoRA SFT narrows that base model to one job—structured budget intents—not open-ended chat. Larger models can be smarter in general, but they are harder to justify for privacy-first, offline-friendly mobile installs.
 **Published artifact:** fused, quantized **GGUF only** — LoRA adapter weights are **not** in this repository.
 **Recommended download:** [`aibudget-intent-05b-v5-Q4_K_M.gguf`](https://huggingface.co/pablitoelpeligro/aibudget-intent-05b-GGUF/resolve/main/aibudget-intent-05b-v5-Q4_K_M.gguf) (~398 MB, SHA256 `d3d6fc0780359cd4d198e9354b9ecfc0ff9cfb0fb729f040326fa6e2f02c693a`)