Instructions to use aelgendy/QModel 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 aelgendy/QModel 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 aelgendy/QModel:Q4_K_M # Run inference directly in the terminal: llama cli -hf aelgendy/QModel:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aelgendy/QModel:Q4_K_M # Run inference directly in the terminal: llama cli -hf aelgendy/QModel: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 aelgendy/QModel:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aelgendy/QModel: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 aelgendy/QModel:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aelgendy/QModel:Q4_K_M
Use Docker
docker model run hf.co/aelgendy/QModel:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use aelgendy/QModel with Ollama:
ollama run hf.co/aelgendy/QModel:Q4_K_M
- Unsloth Studio
How to use aelgendy/QModel 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 aelgendy/QModel 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 aelgendy/QModel to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aelgendy/QModel to start chatting
- Pi
How to use aelgendy/QModel with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aelgendy/QModel: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": "aelgendy/QModel:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use aelgendy/QModel with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aelgendy/QModel: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 "aelgendy/QModel: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 aelgendy/QModel with Docker Model Runner:
docker model run hf.co/aelgendy/QModel:Q4_K_M
- Lemonade
How to use aelgendy/QModel with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aelgendy/QModel:Q4_K_M
Run and chat with the model
lemonade run user.QModel-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use aelgendy/QModel with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aelgendy/QModel: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 aelgendy/QModel:Q4_K_M
Run Hermes
hermes
- Atomic Chat
File size: 1,644 Bytes
eb1414a 15f1210 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | """Async-safe TTL-LRU cache."""
from __future__ import annotations
import asyncio
import hashlib
import json
import time
from collections import OrderedDict
from app.config import cfg
class TTLCache:
"""Async-safe LRU cache with per-entry TTL."""
def __init__(self, maxsize: int = 256, ttl: int = 3600):
self._cache: OrderedDict = OrderedDict()
self._maxsize = maxsize
self._ttl = ttl
self._lock = asyncio.Lock()
def _key(self, *args) -> str:
payload = json.dumps(args, ensure_ascii=False, sort_keys=True)
return hashlib.sha256(payload.encode()).hexdigest()[:20]
async def get(self, *args):
async with self._lock:
k = self._key(*args)
if k in self._cache:
value, ts = self._cache[k]
if time.monotonic() - ts < self._ttl:
self._cache.move_to_end(k)
return value
del self._cache[k]
return None
async def set(self, value, *args):
async with self._lock:
k = self._key(*args)
self._cache[k] = (value, time.monotonic())
self._cache.move_to_end(k)
if len(self._cache) > self._maxsize:
self._cache.popitem(last=False)
search_cache = TTLCache(maxsize=cfg.CACHE_SIZE, ttl=cfg.CACHE_TTL)
analysis_cache = TTLCache(maxsize=cfg.CACHE_SIZE, ttl=cfg.CACHE_TTL)
rewrite_cache = TTLCache(maxsize=cfg.CACHE_SIZE, ttl=cfg.CACHE_TTL * 6)
pipeline_cache = TTLCache(maxsize=128, ttl=cfg.CACHE_TTL)
embed_cache = TTLCache(maxsize=1024, ttl=cfg.CACHE_TTL * 12)
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