Image-Text-to-Text
GGUF
German
English
llama.cpp
mtp
speculative-decoding
qwen3
multimodal
conversational
Instructions to use Davidmg0815/Qwen3.8-27B-MTP-GGUF 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 Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
Use Docker
docker model run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Davidmg0815/Qwen3.8-27B-MTP-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": "Davidmg0815/Qwen3.8-27B-MTP-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
- Ollama
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Ollama:
ollama run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
- Unsloth Desktop
- Pi
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
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": "Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Docker Model Runner:
docker model run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
- Lemonade
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
Run and chat with the model
lemonade run user.Qwen3.8-27B-MTP-GGUF-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
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 "Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0" \ --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"
| { | |
| "modell": "Qwen3.6-27B Uncensored Q6_K_P", | |
| "reasoning": false, | |
| "max_tokens": 900, | |
| "terminal": [ | |
| { | |
| "id": "term01", | |
| "titel": "Zeilen zaehlen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "wc -l < log.txt", | |
| "tps": 30.059044551798177, | |
| "prompt_tps": 164.82773042055672, | |
| "tokens": 7, | |
| "prompt_tokens": 67, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 15, | |
| "abbruch": "stop", | |
| "ttft": 0.406, | |
| "sekunden": 0.69 | |
| }, | |
| { | |
| "id": "term02", | |
| "titel": "Muster mit Zeilennummer", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "grep -n \"WARN\" log.txt", | |
| "tps": 16.793895978608308, | |
| "prompt_tps": 369.4039696267847, | |
| "tokens": 9, | |
| "prompt_tokens": 63, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 22, | |
| "abbruch": "stop", | |
| "ttft": 0.171, | |
| "sekunden": 0.92 | |
| }, | |
| { | |
| "id": "term03", | |
| "titel": "Groesste Datei", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "ls -S | head -n 1", | |
| "tps": 19.370084840971604, | |
| "prompt_tps": 384.5526370988417, | |
| "tokens": 10, | |
| "prompt_tokens": 66, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 17, | |
| "abbruch": "stop", | |
| "ttft": 0.172, | |
| "sekunden": 0.9 | |
| }, | |
| { | |
| "id": "term04", | |
| "titel": "Spalte aus CSV", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "tail -n +2 messwerte.csv | cut -d';' -f1", | |
| "tps": 20.347673921742846, | |
| "prompt_tps": 355.6690649331728, | |
| "tokens": 18, | |
| "prompt_tokens": 83, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 40, | |
| "abbruch": "stop", | |
| "ttft": 0.233, | |
| "sekunden": 1.33 | |
| }, | |
| { | |
| "id": "term05", | |
| "titel": "Doppelte Zeilen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "sort namen.txt | uniq -c | sort -rn", | |
| "tps": 28.917265294221128, | |
| "prompt_tps": 152.13076692630318, | |
| "tokens": 12, | |
| "prompt_tokens": 65, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 35, | |
| "abbruch": "stop", | |
| "ttft": 0.427, | |
| "sekunden": 1.07 | |
| }, | |
| { | |
| "id": "term06", | |
| "titel": "Summe einer Spalte", | |
| "ok": false, | |
| "grund": "Ausgabe falsch: '29'", | |
| "antwort": "awk -F';' 'NR>1{sum+=$2} END{print sum}' messwerte.csv", | |
| "tps": 23.46490627725351, | |
| "prompt_tps": 413.1943140169463, | |
| "tokens": 24, | |
| "prompt_tokens": 77, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 54, | |
| "abbruch": "stop", | |
| "ttft": 0.186, | |
| "sekunden": 1.65 | |
| }, | |
| { | |
| "id": "term07", | |
| "titel": "Numerisch sortieren", | |
| "ok": false, | |
| "grund": "Ausgabe falsch: 'pumpe;6;47.3;ok\\nmotor;6;231.5;ok\\nkompressor;6;18.7;warn\\nvent'", | |
| "antwort": "tail -n +2 messwerte.csv | sort -t ';' -k2 -nr", | |
| "tps": 20.87863906437324, | |
| "prompt_tps": 363.1630275555048, | |
| "tokens": 19, | |
| "prompt_tokens": 89, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 46, | |
| "abbruch": "stop", | |
| "ttft": 0.245, | |
| "sekunden": 1.4 | |
| }, | |
| { | |
| "id": "term08", | |
| "titel": "JSON-Feld lesen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "jq -r '.port' config.json", | |
| "tps": 29.673297000029674, | |
| "prompt_tps": 337.09948367595746, | |
| "tokens": 9, | |
| "prompt_tokens": 60, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 25, | |
| "abbruch": "stop", | |
| "ttft": 0.178, | |
| "sekunden": 0.88 | |
| }, | |
| { | |
| "id": "term09", | |
| "titel": "Ersetzen in Datei", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "sed -i 's/WARN/ACHTUNG/g' log.txt", | |
| "tps": 27.489755770701077, | |
| "prompt_tps": 429.63653796781915, | |
| "tokens": 16, | |
| "prompt_tokens": 82, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 33, | |
| "abbruch": "stop", | |
| "ttft": 0.191, | |
| "sekunden": 1.2 | |
| }, | |
| { | |
| "id": "term10", | |
| "titel": "Gefiltert zaehlen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "awk -F';' 'NR>1 && $2==6{c++}END{print c}' messwerte.csv", | |
| "tps": 23.663038334122103, | |
| "prompt_tps": 452.58545658909713, | |
| "tokens": 27, | |
| "prompt_tokens": 91, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 56, | |
| "abbruch": "stop", | |
| "ttft": 0.201, | |
| "sekunden": 1.79 | |
| } | |
| ], | |
| "code": [ | |
| { | |
| "id": "code01", | |
| "titel": "Intervalle zusammenfassen", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 5, | |
| "gesamt": 5, | |
| "code": "def merge_intervalle(intervalle):\n if not intervalle:\n return []\n \n sortierte = sorted(intervalle, key=lambda x: x[0])\n merged = [sortierte[0]]\n \n for start, ende in sortierte[1:]:\n letzter = merged[-1]\n if start <= letzter[1]:\n merged[-1] = (letzter[0], max(letzter[1], ende))\n else:\n merged.append((start, ende))\n \n return merged", | |
| "tps": 23.37059909252694, | |
| "prompt_tps": 476.44663029278416, | |
| "tokens": 128, | |
| "prompt_tokens": 124, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 419, | |
| "abbruch": "stop", | |
| "ttft": 0.26, | |
| "sekunden": 6.15 | |
| }, | |
| { | |
| "id": "code02", | |
| "titel": "Verschachteltes dict flach machen", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 6, | |
| "gesamt": 6, | |
| "code": "def flatten(d, sep='.'):\n result = {}\n for key, value in d.items():\n if isinstance(value, dict):\n if not value:\n continue\n sub = flatten(value, sep)\n for sub_key, sub_value in sub.items():\n result[f\"{key}{sep}{sub_key}\"] = sub_value\n else:\n result[key] = value\n return result", | |
| "tps": 22.899236983259243, | |
| "prompt_tps": 467.12072810559937, | |
| "tokens": 97, | |
| "prompt_tokens": 93, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 386, | |
| "abbruch": "stop", | |
| "ttft": 0.199, | |
| "sekunden": 4.84 | |
| }, | |
| { | |
| "id": "code03", | |
| "titel": "Roemische Zahlen lesen", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 6, | |
| "gesamt": 6, | |
| "code": "def roemisch_zu_int(s: str) -> int:\n romer_werte = {\n 'I': 1,\n 'V': 5,\n 'X': 10,\n 'L': 50,\n 'C': 100,\n 'D': 500,\n 'M': 1000\n }\n \n ergebnis = 0\n i = 0\n \n while i < len(s):\n # Wenn es ein naechstes Zeichen gibt und der aktuelle Wert kleiner ist\n if i + 1 < len(s) and romer_werte[s[i]] < romer_werte[s[i + 1]]:\n ergebnis += romer_werte[s[i + 1]] - romer_werte[s[i]]\n i += 2\n else:\n ergebnis += romer_werte[s[i]]\n i += 1\n \n return ergebnis", | |
| "tps": 22.73269413038807, | |
| "prompt_tps": 433.78756375929277, | |
| "tokens": 225, | |
| "prompt_tokens": 87, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 599, | |
| "abbruch": "stop", | |
| "ttft": 0.201, | |
| "sekunden": 10.55 | |
| }, | |
| { | |
| "id": "code04", | |
| "titel": "Wortfrequenz mit Gleichstand", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 5, | |
| "gesamt": 5, | |
| "code": "import re\nfrom collections import Counter\n\ndef top_woerter(text, n):\n # Extrahiere alle Wörter (Folgen aus Buchstaben und Ziffern)\n woerter = re.findall(r'[a-zA-Z0-9]+', text)\n \n # Konvertiere alle Wörter in Kleinbuchstaben\n woerter = [wort.lower() for wort in woerter]\n \n # Zähle die Häufigkeit der Wörter\n zaehler = Counter(woerter)\n \n # Sortiere nach Anzahl (absteigend) und dann alphabetisch (aufsteigend)\n sortierte_woerter = sorted(zaehler.items(), key=lambda x: (-x[1], x[0]))\n \n # Gib die n häufigsten Wörter zurück\n return sortierte_woerter[:n]", | |
| "tps": 22.385186379061793, | |
| "prompt_tps": 432.39570383982834, | |
| "tokens": 190, | |
| "prompt_tokens": 112, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 609, | |
| "abbruch": "stop", | |
| "ttft": 0.259, | |
| "sekunden": 9.01 | |
| }, | |
| { | |
| "id": "code05", | |
| "titel": "Klammern pruefen", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 7, | |
| "gesamt": 7, | |
| "code": "def klammern_ok(s):\n stack = []\n matching = {')': '(', ']': '[', '}': '{'}\n for char in s:\n if char in matching.values():\n stack.append(char)\n elif char in matching:\n if not stack or stack.pop() != matching[char]:\n return False\n return not stack", | |
| "tps": 22.568406832439138, | |
| "prompt_tps": 444.0833463698709, | |
| "tokens": 85, | |
| "prompt_tokens": 88, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 322, | |
| "abbruch": "stop", | |
| "ttft": 0.198, | |
| "sekunden": 4.19 | |
| }, | |
| { | |
| "id": "code06", | |
| "titel": "Suche im rotierten Feld", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 6, | |
| "gesamt": 6, | |
| "code": "def suche_rotiert(arr, ziel):\n if not arr:\n return -1\n \n links, rechts = 0, len(arr) - 1\n \n while links <= rechts:\n mitte = (links + rechts) // 2\n \n if arr[mitte] == ziel:\n return mitte\n \n # Bestimme, welcher Teil sortiert ist\n if arr[links] <= arr[mitte]:\n # Linker Teil ist sortiert\n if arr[links] <= ziel < arr[mitte]:\n rechts = mitte - 1\n else:\n links = mitte + 1\n else:\n # Rechter Teil ist sortiert\n if arr[mitte] < ziel <= arr[", | |
| "tps": 23.08083179530167, | |
| "prompt_tps": 277.85495971103086, | |
| "tokens": 195, | |
| "prompt_tokens": 105, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 728, | |
| "abbruch": "stop", | |
| "ttft": 0.378, | |
| "sekunden": 9.05 | |
| }, | |
| { | |
| "id": "code07", | |
| "titel": "Werktage zwischen zwei Daten", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 5, | |
| "gesamt": 5, | |
| "code": "from datetime import datetime, timedelta\n\ndef werktage(von, bis):\n if not isinstance(von, str) or not isinstance(bis, str):\n return 0\n \n try:\n start_date = datetime.strptime(von, '%Y-%m-%d')\n end_date = datetime.strptime(bis, '%Y-%m-%d')\n except ValueError:\n return 0\n \n if end_date < start_date:\n return 0\n \n count = 0\n current_date = start_date\n while current_date <= end_date:\n if current_date.weekday() < 5: # Monday to Friday\n count += 1\n current_date += timedelta(days=1)\n \n return count", | |
| "tps": 23.126009586148914, | |
| "prompt_tps": 262.2263012980202, | |
| "tokens": 166, | |
| "prompt_tokens": 90, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 604, | |
| "abbruch": "stop", | |
| "ttft": 0.343, | |
| "sekunden": 7.75 | |
| }, | |
| { | |
| "id": "code08", | |
| "titel": "LRU-Zwischenspeicher", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 5, | |
| "gesamt": 5, | |
| "code": "class LRU:\n def __init__(self, groesse):\n self.groesse = groesse\n self.cache = {}\n self.order = []\n\n def get(self, schluessel):\n if schluessel in self.cache:\n self.order.remove(schluessel)\n self.order.append(schluessel)\n return self.cache[schluessel]\n return None\n\n def put(self, schluessel, wert):\n if schluessel in self.cache:\n self.order.remove(schluessel)\n elif len(self.cache) >= self.groesse:\n oldest = self.order.pop(0)\n del self.cache[oldest]\n self.cache[schlue", | |
| "tps": 23.19626009646056, | |
| "prompt_tps": 506.39851659502204, | |
| "tokens": 181, | |
| "prompt_tokens": 133, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 664, | |
| "abbruch": "stop", | |
| "ttft": 0.263, | |
| "sekunden": 8.28 | |
| }, | |
| { | |
| "id": "code09", | |
| "titel": "Wiederholung nach Fehlschlag", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 4, | |
| "gesamt": 4, | |
| "code": "def wiederhole(fn, versuche=3):\n for i in range(versuche):\n try:\n return fn()\n except Exception as e:\n if i == versuche - 1:\n raise", | |
| "tps": 24.525379168931245, | |
| "prompt_tps": 502.50065182867576, | |
| "tokens": 56, | |
| "prompt_tokens": 106, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 199, | |
| "abbruch": "stop", | |
| "ttft": 0.211, | |
| "sekunden": 2.99 | |
| }, | |
| { | |
| "id": "code10", | |
| "titel": "CSV sortieren und als JSON", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 3, | |
| "gesamt": 3, | |
| "code": "import csv\nimport json\nimport io\n\ndef csv_nach_json(text, spalte):\n reader = csv.DictReader(io.StringIO(text), delimiter=';')\n rows = list(reader)\n \n # Check if all values in the specified column are numeric\n try:\n for row in rows:\n float(row[spalte])\n numeric = True\n except (ValueError, KeyError):\n numeric = False\n \n if numeric:\n rows.sort(key=lambda x: float(x[spalte]))\n else:\n rows.sort(key=lambda x: x[spalte])\n \n return json.dumps(rows, ensure_ascii=False)", | |
| "tps": 22.718219628388255, | |
| "prompt_tps": 556.4025725952017, | |
| "tokens": 148, | |
| "prompt_tokens": 127, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 557, | |
| "abbruch": "stop", | |
| "ttft": 0.228, | |
| "sekunden": 6.96 | |
| } | |
| ], | |
| "messungen": [ | |
| { | |
| "tps": 30.059044551798177, | |
| "prompt_tps": 164.82773042055672, | |
| "tokens": 7, | |
| "prompt_tokens": 67, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 15, | |
| "abbruch": "stop", | |
| "ttft": 0.406, | |
| "sekunden": 0.69 | |
| }, | |
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