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": "Qwen3.8-27B Q8_0", | |
| "reasoning": false, | |
| "max_tokens": 900, | |
| "terminal": [ | |
| { | |
| "id": "term01", | |
| "titel": "Zeilen zaehlen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "wc -l < log.txt", | |
| "tps": 25.809115779693393, | |
| "prompt_tps": 296.59666395155296, | |
| "tokens": 7, | |
| "prompt_tokens": 67, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 15, | |
| "abbruch": "stop", | |
| "ttft": 0.226, | |
| "sekunden": 0.55 | |
| }, | |
| { | |
| "id": "term02", | |
| "titel": "Muster mit Zeilennummer", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "grep -n \"WARN\" log.txt", | |
| "tps": 25.087667460180295, | |
| "prompt_tps": 360.0658406108546, | |
| "tokens": 9, | |
| "prompt_tokens": 63, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 22, | |
| "abbruch": "stop", | |
| "ttft": 0.175, | |
| "sekunden": 0.93 | |
| }, | |
| { | |
| "id": "term03", | |
| "titel": "Groesste Datei", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "ls -S | head -n 1", | |
| "tps": 25.06680303007515, | |
| "prompt_tps": 379.87360569119727, | |
| "tokens": 10, | |
| "prompt_tokens": 66, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 17, | |
| "abbruch": "stop", | |
| "ttft": 0.174, | |
| "sekunden": 0.98 | |
| }, | |
| { | |
| "id": "term04", | |
| "titel": "Spalte aus CSV", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "awk -F';' 'NR>1 {print $1}' messwerte.csv", | |
| "tps": 19.915315652221057, | |
| "prompt_tps": 448.72384021106245, | |
| "tokens": 18, | |
| "prompt_tokens": 83, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 41, | |
| "abbruch": "stop", | |
| "ttft": 0.185, | |
| "sekunden": 1.53 | |
| }, | |
| { | |
| "id": "term05", | |
| "titel": "Doppelte Zeilen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "sort namen.txt | uniq -c | sort -rn", | |
| "tps": 19.598044115197304, | |
| "prompt_tps": 373.5181386154545, | |
| "tokens": 12, | |
| "prompt_tokens": 65, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 35, | |
| "abbruch": "stop", | |
| "ttft": 0.174, | |
| "sekunden": 1.0 | |
| }, | |
| { | |
| "id": "term06", | |
| "titel": "Summe einer Spalte", | |
| "ok": false, | |
| "grund": "Ausgabe falsch: '0'", | |
| "antwort": "awk -F';' 'NR>1 {sum+=$spannung} END {print sum}' messwerte.csv", | |
| "tps": 20.73713032954618, | |
| "prompt_tps": 431.39913384018064, | |
| "tokens": 25, | |
| "prompt_tokens": 77, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 63, | |
| "abbruch": "stop", | |
| "ttft": 0.178, | |
| "sekunden": 1.65 | |
| }, | |
| { | |
| "id": "term07", | |
| "titel": "Numerisch sortieren", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "tail -n +2 messwerte.csv | sort -t';' -k$(head -1 messwerte.csv | tr ';' '\\n' | grep -n '^spannung$' | cut -d: -f1) -rn", | |
| "tps": 19.134859427668697, | |
| "prompt_tps": 479.73005751370465, | |
| "tokens": 50, | |
| "prompt_tokens": 89, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 119, | |
| "abbruch": "stop", | |
| "ttft": 0.186, | |
| "sekunden": 3.21 | |
| }, | |
| { | |
| "id": "term08", | |
| "titel": "JSON-Feld lesen", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "jq -r '.port' config.json", | |
| "tps": 25.32186909156388, | |
| "prompt_tps": 347.9854542080141, | |
| "tokens": 9, | |
| "prompt_tokens": 60, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 25, | |
| "abbruch": "stop", | |
| "ttft": 0.172, | |
| "sekunden": 0.76 | |
| }, | |
| { | |
| "id": "term09", | |
| "titel": "Ersetzen in Datei", | |
| "ok": true, | |
| "grund": "", | |
| "antwort": "sed -i 's/WARN/ACHTUNG/g' log.txt", | |
| "tps": 19.551942791015396, | |
| "prompt_tps": 456.14347381068933, | |
| "tokens": 16, | |
| "prompt_tokens": 82, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 33, | |
| "abbruch": "stop", | |
| "ttft": 0.18, | |
| "sekunden": 1.39 | |
| }, | |
| { | |
| "id": "term10", | |
| "titel": "Gefiltert zaehlen", | |
| "ok": false, | |
| "grund": "Ausgabe falsch: '0'", | |
| "antwort": "awk -F';' 'NR>1 && $kanal==6' messwerte.csv | wc -l", | |
| "tps": 20.754495077725583, | |
| "prompt_tps": 492.1153388565619, | |
| "tokens": 24, | |
| "prompt_tokens": 91, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 51, | |
| "abbruch": "stop", | |
| "ttft": 0.185, | |
| "sekunden": 1.62 | |
| } | |
| ], | |
| "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 sortiert = sorted(intervalle, key=lambda x: x[0])\n erg = [sortiert[0]]\n for start, ende in sortiert[1:]:\n if start <= erg[-1][1]:\n erg[-1] = (erg[-1][0], max(erg[-1][1], ende))\n else:\n erg.append((start, ende))\n return erg", | |
| "tps": 19.30488463871395, | |
| "prompt_tps": 592.799399551576, | |
| "tokens": 119, | |
| "prompt_tokens": 124, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 360, | |
| "abbruch": "stop", | |
| "ttft": 0.209, | |
| "sekunden": 6.76 | |
| }, | |
| { | |
| "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 value:\n for k, v in flatten(value, sep).items():\n result[f\"{key}{sep}{k}\"] = v\n # leeres dict wird ignoriert\n else:\n result[key] = value\n return result", | |
| "tps": 20.265722427730065, | |
| "prompt_tps": 271.0990881742497, | |
| "tokens": 94, | |
| "prompt_tokens": 93, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 356, | |
| "abbruch": "stop", | |
| "ttft": 0.343, | |
| "sekunden": 5.2 | |
| }, | |
| { | |
| "id": "code03", | |
| "titel": "Roemische Zahlen lesen", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 6, | |
| "gesamt": 6, | |
| "code": "def roemisch_zu_int(s):\n romane = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}\n total = 0\n for i in range(len(s)):\n if i < len(s) - 1 and romane[s[i]] < romane[s[i + 1]]:\n total -= romane[s[i]]\n else:\n total += romane[s[i]]\n return total", | |
| "tps": 19.577864618361925, | |
| "prompt_tps": 467.244546128315, | |
| "tokens": 139, | |
| "prompt_tokens": 87, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 320, | |
| "abbruch": "stop", | |
| "ttft": 0.186, | |
| "sekunden": 7.7 | |
| }, | |
| { | |
| "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 woerter = re.findall(r'[a-zA-Z0-9]+', text)\n woerter = [w.lower() for w in woerter]\n zaehler = Counter(woerter)\n sortiert = sorted(zaehler.items(), key=lambda x: (-x[1], x[0]))\n return sortiert[:n]", | |
| "tps": 19.5525206331296, | |
| "prompt_tps": 261.4549970586313, | |
| "tokens": 103, | |
| "prompt_tokens": 112, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 296, | |
| "abbruch": "stop", | |
| "ttft": 0.428, | |
| "sekunden": 5.91 | |
| }, | |
| { | |
| "id": "code05", | |
| "titel": "Klammern pruefen", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 7, | |
| "gesamt": 7, | |
| "code": "def klammern_ok(s):\n stack = []\n pairs = {')': '(', ']': '[', '}': '{'}\n for ch in s:\n if ch in '([{':\n stack.append(ch)\n elif ch in ')]}':\n if not stack or stack[-1] != pairs[ch]:\n return False\n stack.pop()\n return not stack", | |
| "tps": 20.084597187206157, | |
| "prompt_tps": 203.9978951126277, | |
| "tokens": 93, | |
| "prompt_tokens": 88, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 313, | |
| "abbruch": "stop", | |
| "ttft": 0.431, | |
| "sekunden": 5.28 | |
| }, | |
| { | |
| "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 # Linker Teil ist sortiert\n if arr[links] <= arr[mitte]:\n if arr[links] <= ziel < arr[mitte]:\n rechts = mitte - 1\n else:\n links = mitte + 1\n # Rechter Teil ist sortiert\n else:\n if arr[mitte] < ziel <= arr[rechts]:\n links = mitte + 1\n ", | |
| "tps": 18.812744080017964, | |
| "prompt_tps": 537.1640806053072, | |
| "tokens": 184, | |
| "prompt_tokens": 105, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 674, | |
| "abbruch": "stop", | |
| "ttft": 0.195, | |
| "sekunden": 10.46 | |
| }, | |
| { | |
| "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 bis < von:\n return 0\n start = datetime.strptime(von, '%Y-%m-%d')\n end = datetime.strptime(bis, '%Y-%m-%d')\n count = 0\n current = start\n while current <= end:\n if current.weekday() < 5:\n count += 1\n current += timedelta(days=1)\n return count", | |
| "tps": 19.427207531485283, | |
| "prompt_tps": 486.82858224698435, | |
| "tokens": 114, | |
| "prompt_tokens": 90, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 377, | |
| "abbruch": "stop", | |
| "ttft": 0.185, | |
| "sekunden": 6.28 | |
| }, | |
| { | |
| "id": "code08", | |
| "titel": "LRU-Zwischenspeicher", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 5, | |
| "gesamt": 5, | |
| "code": "from collections import OrderedDict\n\nclass LRU:\n def __init__(self, groesse):\n self.groesse = groesse\n self.cache = OrderedDict()\n\n def get(self, schluessel):\n if schluessel not in self.cache:\n return None\n self.cache.move_to_end(schluessel)\n return self.cache[schluessel]\n\n def put(self, schluessel, wert):\n if schluessel in self.cache:\n self.cache.move_to_end(schluessel)\n self.cache[schluessel] = wert\n if len(self.cache) > self.groesse:\n self.cache.popitem(last=False)", | |
| "tps": 19.464508901082485, | |
| "prompt_tps": 309.25198223545004, | |
| "tokens": 156, | |
| "prompt_tokens": 133, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 584, | |
| "abbruch": "stop", | |
| "ttft": 0.43, | |
| "sekunden": 8.66 | |
| }, | |
| { | |
| "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:\n if i == versuche - 1:\n raise", | |
| "tps": 18.304271911993062, | |
| "prompt_tps": 540.9156783899084, | |
| "tokens": 54, | |
| "prompt_tokens": 106, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 194, | |
| "abbruch": "stop", | |
| "ttft": 0.196, | |
| "sekunden": 3.37 | |
| }, | |
| { | |
| "id": "code10", | |
| "titel": "CSV sortieren und als JSON", | |
| "ok": true, | |
| "grund": "", | |
| "bestanden": 3, | |
| "gesamt": 3, | |
| "code": "import json\nimport csv\nimport io\n\ndef csv_nach_json(text, spalte):\n reader = csv.DictReader(io.StringIO(text), delimiter=';')\n rows = list(reader)\n \n if not rows:\n return json.dumps([])\n \n # Prüfen, ob alle Werte in der Spalte numerisch sind\n try:\n for row in rows:\n val = row[spalte].strip()\n if val == '':\n continue\n float(val)\n numeric = True\n except (ValueError, TypeError):\n numeric = False\n \n if numeric:\n rows.sort(key=lambda r: float(r[spalte].strip()) if r[spalte].strip() != ''", | |
| "tps": 18.953469325714536, | |
| "prompt_tps": 595.1348894314353, | |
| "tokens": 204, | |
| "prompt_tokens": 127, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 756, | |
| "abbruch": "stop", | |
| "ttft": 0.213, | |
| "sekunden": 11.37 | |
| } | |
| ], | |
| "messungen": [ | |
| { | |
| "tps": 25.809115779693393, | |
| "prompt_tps": 296.59666395155296, | |
| "tokens": 7, | |
| "prompt_tokens": 67, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 15, | |
| "abbruch": "stop", | |
| "ttft": 0.226, | |
| "sekunden": 0.55 | |
| }, | |
| { | |
| "tps": 25.087667460180295, | |
| "prompt_tps": 360.0658406108546, | |
| "tokens": 9, | |
| "prompt_tokens": 63, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 22, | |
| "abbruch": "stop", | |
| "ttft": 0.175, | |
| "sekunden": 0.93 | |
| }, | |
| { | |
| "tps": 25.06680303007515, | |
| "prompt_tps": 379.87360569119727, | |
| "tokens": 10, | |
| "prompt_tokens": 66, | |
| "denk_zeichen": 0, | |
| "antwort_zeichen": 17, | |
| "abbruch": "stop", | |
| "ttft": 0.174, | |
| "sekunden": 0.98 | |
| }, | |
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