Instructions to use Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack:Q4_K_M # Run inference directly in the terminal: llama cli -hf Neohosseinism/gemma4-stack:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Neohosseinism/gemma4-stack:Q4_K_M # Run inference directly in the terminal: llama cli -hf Neohosseinism/gemma4-stack: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 Neohosseinism/gemma4-stack:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Neohosseinism/gemma4-stack: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 Neohosseinism/gemma4-stack:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Neohosseinism/gemma4-stack:Q4_K_M
Use Docker
docker model run hf.co/Neohosseinism/gemma4-stack:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Neohosseinism/gemma4-stack with Ollama:
ollama run hf.co/Neohosseinism/gemma4-stack:Q4_K_M
- Unsloth Studio
How to use Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Neohosseinism/gemma4-stack to start chatting
- Pi
How to use Neohosseinism/gemma4-stack with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Neohosseinism/gemma4-stack: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": "Neohosseinism/gemma4-stack:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use Neohosseinism/gemma4-stack with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Neohosseinism/gemma4-stack: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 "Neohosseinism/gemma4-stack: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 Neohosseinism/gemma4-stack with Docker Model Runner:
docker model run hf.co/Neohosseinism/gemma4-stack:Q4_K_M
- Lemonade
How to use Neohosseinism/gemma4-stack with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Neohosseinism/gemma4-stack:Q4_K_M
Run and chat with the model
lemonade run user.gemma4-stack-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Neohosseinism/gemma4-stack with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Neohosseinism/gemma4-stack: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 Neohosseinism/gemma4-stack:Q4_K_M
Run Hermes
hermes
- Atomic Chat
File size: 8,895 Bytes
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title: Gemma 4 Omni (Audio)
author: gemma4-stack
version: 0.1.0
required_open_webui_version: 0.5.0
description: >
Sends an attached audio clip to Gemma 4 as a NATIVE multimodal input
(OpenAI `input_audio` content part) via llama-swap/llama-server — the model
"hears" the audio instead of transcribing it with Whisper. Resamples to
16 kHz mono first (Gemma 4's reliable envelope, clips <= ~30 s).
HOW TO USE
Admin → Functions → "+" → paste this file → Save → enable it.
In a new chat pick the model "Gemma 4 · Omni (audio)", attach a short audio
clip, type your question (Persian or any language), send.
FRAGILE BIT (read me)
Open WebUI hands a Pipe only FILE REFERENCES, not bytes. _resolve_local_path()
below fetches the real file from Open WebUI's store. That store API moves
between versions — if audio isn't found, adjust _resolve_local_path() to your
installed Open WebUI version first (see the strategies inside).
"""
import os
import json
import base64
import glob
import shutil
import subprocess
import tempfile
from typing import List, Optional
import requests
from pydantic import BaseModel, Field
AUDIO_EXTS = (".wav", ".mp3", ".flac", ".ogg", ".m4a", ".webm", ".aac", ".opus")
DATA_DIR = os.environ.get("DATA_DIR", "/app/backend/data")
UPLOADS_DIR = os.path.join(DATA_DIR, "uploads")
class Pipe:
class Valves(BaseModel):
LLAMASWAP_URL: str = Field(
default="http://llama-swap:8080/v1",
description="OpenAI-compatible base URL of llama-swap.",
)
MODEL: str = Field(
default="gemma-e4b",
description="llama-swap model key (audio-capable: gemma-e4b/gemma-12b).",
)
API_KEY: str = Field(default="sk-local", description="Bearer key for llama-swap.")
TEMPERATURE: float = Field(default=1.0)
TOP_K: int = Field(default=64)
TOP_P: float = Field(default=0.95)
MAX_TOKENS: int = Field(default=512)
TARGET_SR: int = Field(default=16000, description="Resample rate (Gemma wants 16 kHz mono).")
DEFAULT_PROMPT: str = Field(
default="این فایل صوتی را دقیق بنویس و در صورت نیاز توضیح بده.",
description="Used when the user attaches audio without typing a question.",
)
def __init__(self):
self.valves = self.Valves()
def pipes(self):
return [{"id": "gemma4-audio", "name": "Gemma 4 · Omni (audio)"}]
# ------------------------------------------------------------------ helpers
def _collect_file_refs(self, body, __files__, __metadata__) -> List[dict]:
refs = []
if __files__:
refs += __files__
if isinstance(__metadata__, dict):
refs += __metadata__.get("files", []) or []
meta = (body or {}).get("metadata", {}) or {}
refs += meta.get("files", []) or []
refs += (body or {}).get("files", []) or []
return refs
def _ref_id_and_name(self, ref: dict):
# Open WebUI nests the actual record under "file" in some versions.
inner = ref.get("file", ref) if isinstance(ref, dict) else {}
fid = ref.get("id") or inner.get("id")
name = (
ref.get("name")
or inner.get("filename")
or (inner.get("meta") or {}).get("name")
or ""
)
ctype = (inner.get("meta") or {}).get("content_type", "") or ref.get("type", "")
return fid, name, ctype
def _resolve_local_path(self, fid, name) -> Optional[str]:
"""Turn a file reference into a real on-disk path. Version-sensitive."""
# Strategy 1: official Files model.
try:
from open_webui.models.files import Files # type: ignore
rec = Files.get_file_by_id(fid)
if rec is not None:
p = getattr(rec, "path", None) or (getattr(rec, "meta", {}) or {}).get("path")
if p:
if not os.path.isabs(p):
p = os.path.join(DATA_DIR, p)
if os.path.exists(p):
return p
except Exception:
pass
# Strategy 2: storage provider abstraction.
try:
from open_webui.storage.provider import Storage # type: ignore
p = Storage.get_file(f"uploads/{fid}") # may raise / vary
if p and os.path.exists(p):
return p
except Exception:
pass
# Strategy 3: scan the uploads dir for <id> or <name>.
for pattern in (f"*{fid}*", f"*{name}*"):
if not pattern.strip("*"):
continue
hits = glob.glob(os.path.join(UPLOADS_DIR, pattern))
hits = [h for h in hits if os.path.isfile(h)]
if hits:
return max(hits, key=os.path.getmtime)
return None
def _to_16k_mono_wav(self, src: str) -> (str, str):
"""Return (path, format). Resample via ffmpeg if available, else pass through."""
ffmpeg = shutil.which("ffmpeg")
if ffmpeg:
out = tempfile.NamedTemporaryFile(suffix=".wav", delete=False).name
try:
subprocess.run(
[ffmpeg, "-y", "-i", src, "-ar", str(self.valves.TARGET_SR),
"-ac", "1", "-f", "wav", out],
check=True, capture_output=True,
)
return out, "wav"
except Exception:
pass
ext = os.path.splitext(src)[1].lower().lstrip(".") or "wav"
return src, ("wav" if ext not in ("mp3", "flac", "wav") else ext)
def _latest_user_text(self, body) -> str:
for msg in reversed((body or {}).get("messages", [])):
if msg.get("role") == "user":
c = msg.get("content")
if isinstance(c, str):
return c.strip()
if isinstance(c, list):
parts = [p.get("text", "") for p in c if p.get("type") == "text"]
return " ".join(t for t in parts if t).strip()
return ""
# --------------------------------------------------------------------- main
def pipe(self, body: dict, __user__=None, __request__=None,
__files__=None, __metadata__=None):
refs = self._collect_file_refs(body, __files__, __metadata__)
audio_paths = []
for ref in refs:
fid, name, ctype = self._ref_id_and_name(ref)
is_audio = ctype.startswith("audio") or name.lower().endswith(AUDIO_EXTS)
if not is_audio:
continue
local = self._resolve_local_path(fid, name)
if local:
audio_paths.append(local)
if not audio_paths:
return (
"⚠️ No audio found. Attach a short clip (≤ ~30 s) and ask your "
"question. If you *did* attach audio, the file-store lookup needs "
"adapting to your Open WebUI version — see _resolve_local_path()."
)
text = self._latest_user_text(body) or self.valves.DEFAULT_PROMPT
content = [{"type": "text", "text": text}]
for p in audio_paths:
wav, fmt = self._to_16k_mono_wav(p)
with open(wav, "rb") as f:
content.append({
"type": "input_audio",
"input_audio": {
"data": base64.b64encode(f.read()).decode("ascii"),
"format": fmt,
},
})
payload = {
"model": self.valves.MODEL,
"messages": [{"role": "user", "content": content}],
"temperature": self.valves.TEMPERATURE,
"top_k": self.valves.TOP_K,
"top_p": self.valves.TOP_P,
"max_tokens": self.valves.MAX_TOKENS,
"stream": True,
}
headers = {"Authorization": f"Bearer {self.valves.API_KEY}"}
url = self.valves.LLAMASWAP_URL.rstrip("/") + "/chat/completions"
def gen():
with requests.post(url, json=payload, headers=headers,
stream=True, timeout=600) as r:
r.raise_for_status()
for line in r.iter_lines(decode_unicode=True):
if not line or not line.startswith("data:"):
continue
data = line[len("data:"):].strip()
if data == "[DONE]":
break
try:
delta = json.loads(data)["choices"][0]["delta"]
piece = delta.get("content")
if piece:
yield piece
except Exception:
continue
return gen()
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