Instructions to use nphearum/PsarAI-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nphearum/PsarAI-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nphearum/PsarAI-2B-GGUF",
	filename="PsarAI-2B.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nphearum/PsarAI-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nphearum/PsarAI-2B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use nphearum/PsarAI-2B-GGUF with Ollama:
```
ollama run hf.co/nphearum/PsarAI-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nphearum/PsarAI-2B-GGUF to start chatting

How to use nphearum/PsarAI-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nphearum/PsarAI-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nphearum/PsarAI-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-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 nphearum/PsarAI-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use nphearum/PsarAI-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nphearum/PsarAI-2B-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 "nphearum/PsarAI-2B-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"

Docker Model Runner
How to use nphearum/PsarAI-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/nphearum/PsarAI-2B-GGUF:Q4_K_M
```

Lemonade

How to use nphearum/PsarAI-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nphearum/PsarAI-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PsarAI-2B-GGUF-Q4_K_M

List all available models

lemonade list

PsarAI-2B GGUF

GGUF exports for PsarAI-2B, a PsarAI chat model based on unsloth/gemma-4-E2B-it.

The chat template identifies the assistant as PsarAI and uses Gemma's native turn, channel, tool-call, image, audio, and video tokens.

Recommended File

Use Q4_K_M for the best default balance of size, speed, and quality:

llama-cli \
  -hf nphearum/PsarAI-2B-GGUF:Q4_K_M \
  -p "Who created you?"

For higher quality, use Q5_K_M, Q6_K, or Q8_0 if you have enough RAM/VRAM.

llama.cpp Server

llama-server \
  -hf nphearum/PsarAI-2B-GGUF:Q4_K_M \
  --host 0.0.0.0 \
  --port 8080 \
  -c 8192

Then call the OpenAI-compatible endpoint:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "PsarAI-2B",
    "messages": [
      {"role": "user", "content": "Who created you?"}
    ],
    "temperature": 0.7,
    "top_p": 0.9
  }'

Thinking

For normal chatbot use, disable visible thinking in your template/runtime settings when supported:

{"enable_thinking": false}

If thinking is enabled, the template asks the model to keep it short and useful.

Multimodal Projector

This repo includes:

PsarAI-2B.BF16-mmproj.gguf

Use it with a llama.cpp build/runtime that supports Gemma 4 multimodal GGUF. Exact image/audio/video CLI flags may depend on your llama.cpp version.

Files

File	Size
`PsarAI-2B.Q4_K_M.gguf`	3.19 GiB
`PsarAI-2B.Q5_K_M.gguf`	3.38 GiB
`PsarAI-2B.Q6_K.gguf`	3.58 GiB
`PsarAI-2B.Q8_0.gguf`	4.63 GiB
`PsarAI-2B.Q4_K_S.gguf`	3.13 GiB
`PsarAI-2B.Q3_K_M.gguf`	2.98 GiB
`PsarAI-2B.IQ4_XS.gguf`	3.08 GiB
`PsarAI-2B.IQ4_NL.gguf`	3.14 GiB
`PsarAI-2B.IQ3_M.gguf`	2.14 GiB
`PsarAI-2B.F16.gguf`	8.67 GiB
`PsarAI-2B.BF16-mmproj.gguf`	0.92 GiB

Quantization Guide

Q4_K_M: recommended default
Q5_K_M: better quality with moderate extra size
Q6_K: strong quality if memory is available
Q8_0: near full precision, largest practical runtime file
Q3_* / IQ3_*: smaller files, lower quality

Notes

Context length in the source config is up to 131072 tokens, but practical context depends on your runtime memory.
This model uses a Gemma-style chat template, not Qwen XML-style tool calls.
If you upload only a subset of files, update the file table above to match the repo contents.

Downloads last month: -

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for nphearum/PsarAI-2B-GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

unsloth/gemma-4-E2B-it

Quantized

(21)

this model