Instructions to use nikhilkeetha/fynuu-0.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nikhilkeetha/fynuu-0.5b-gguf with MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir fynuu-0.5b-gguf nikhilkeetha/fynuu-0.5b-gguf

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nikhilkeetha/fynuu-0.5b-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 nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nikhilkeetha/fynuu-0.5b-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 nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nikhilkeetha/fynuu-0.5b-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 nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nikhilkeetha/fynuu-0.5b-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 nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M

Use Docker

docker model run hf.co/nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use nikhilkeetha/fynuu-0.5b-gguf with Ollama:
```
ollama run hf.co/nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M
```

Unsloth Studio

How to use nikhilkeetha/fynuu-0.5b-gguf 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 nikhilkeetha/fynuu-0.5b-gguf 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 nikhilkeetha/fynuu-0.5b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nikhilkeetha/fynuu-0.5b-gguf to start chatting

How to use nikhilkeetha/fynuu-0.5b-gguf with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nikhilkeetha/fynuu-0.5b-gguf"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nikhilkeetha/fynuu-0.5b-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nikhilkeetha/fynuu-0.5b-gguf with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nikhilkeetha/fynuu-0.5b-gguf"

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 nikhilkeetha/fynuu-0.5b-gguf

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use nikhilkeetha/fynuu-0.5b-gguf with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nikhilkeetha/fynuu-0.5b-gguf"

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 "nikhilkeetha/fynuu-0.5b-gguf" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Docker Model Runner
How to use nikhilkeetha/fynuu-0.5b-gguf with Docker Model Runner:
```
docker model run hf.co/nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M
```

Lemonade

How to use nikhilkeetha/fynuu-0.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nikhilkeetha/fynuu-0.5b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.fynuu-0.5b-gguf-Q4_K_M

List all available models

lemonade list

Fynuu 1.5B — GGUF (Q4_K_M)

Fine-tuned Qwen2.5-1.5B-Instruct for Fynuu, a personal AI companion Android app.

Model Details

Property	Value
Base model	Qwen2.5-1.5B-Instruct
Fine-tuning	LoRA (rank 16, 16 layers)
Training data	~5,000 curated samples
Quantization	Q4_K_M (4-bit, 5.08 BPW)
File size	~940 MB
Training framework	MLX on Apple Silicon

Usage

This model is designed for the Fynuu Android app and outputs structured XML responses:

<t>tone</t><r>response text</r><f>functionName(params)</f><s>suggestion1|suggestion2</s>

<t> — emotion/tone (33 valid tones)
<r> — response text (≤12 words, no emojis)
<f> — optional function call (10 functions: setAlarm, getWeather, etc.)
<s> — optional quick-reply suggestions
<cr> — cloud routing trigger for complex queries

Performance

Metric	Score
Test benchmark	15/15 (100%)
Structural pass rate	See eval results
Training time	~100 min on M4 Air

System Prompt Format

Ur Fynuu, a friendly personal AI companion.
Sys:{dt:2025-03-29 10:00,bat:72%,loc:17.38,78.48}.
Usr:{name:Nick,age:22,nick:Nicky,gen:M,occ:student-cs}.

Inference

Use with llama.cpp or any GGUF-compatible runtime:

llama-cli -m fynuu-q4_k_m.gguf -p "<system prompt>" --temp 0.7 --top-p 0.8 --top-k 20

Version History

Version	Base Model	Date	Notes
v2 (current)	Qwen2.5-1.5B-Instruct	2026-05-10	Major upgrade: 3x larger, proper LoRA, 15/15 test score
v1	Qwen2.5-0.5B-Instruct	2026-05-07	Initial release, limited capacity

Downloads last month: 5

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for nikhilkeetha/fynuu-0.5b-gguf

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Qwen/Qwen2.5-1.5B

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