Instructions to use jacob-ml/jacob-24b-prod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jacob-ml/jacob-24b-prod with PEFT:
Task type is invalid.
- Transformers
How to use jacob-ml/jacob-24b-prod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jacob-ml/jacob-24b-prod") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jacob-ml/jacob-24b-prod", dtype="auto") - llama-cpp-python
How to use jacob-ml/jacob-24b-prod with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jacob-ml/jacob-24b-prod", filename="jacob-24b-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use jacob-ml/jacob-24b-prod with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: llama-cli -hf jacob-ml/jacob-24b-prod:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: llama-cli -hf jacob-ml/jacob-24b-prod: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 jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf jacob-ml/jacob-24b-prod: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 jacob-ml/jacob-24b-prod:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf jacob-ml/jacob-24b-prod:Q4_K_M
Use Docker
docker model run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use jacob-ml/jacob-24b-prod with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jacob-ml/jacob-24b-prod" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jacob-ml/jacob-24b-prod", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- SGLang
How to use jacob-ml/jacob-24b-prod with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "jacob-ml/jacob-24b-prod" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jacob-ml/jacob-24b-prod", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "jacob-ml/jacob-24b-prod" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jacob-ml/jacob-24b-prod", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use jacob-ml/jacob-24b-prod with Ollama:
ollama run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- Unsloth Studio new
How to use jacob-ml/jacob-24b-prod 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 jacob-ml/jacob-24b-prod 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 jacob-ml/jacob-24b-prod to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jacob-ml/jacob-24b-prod to start chatting
- Pi new
How to use jacob-ml/jacob-24b-prod with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf jacob-ml/jacob-24b-prod: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": "jacob-ml/jacob-24b-prod:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jacob-ml/jacob-24b-prod with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf jacob-ml/jacob-24b-prod: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 jacob-ml/jacob-24b-prod:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use jacob-ml/jacob-24b-prod with Docker Model Runner:
docker model run hf.co/jacob-ml/jacob-24b-prod:Q4_K_M
- Lemonade
How to use jacob-ml/jacob-24b-prod with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jacob-ml/jacob-24b-prod:Q4_K_M
Run and chat with the model
lemonade run user.jacob-24b-prod-Q4_K_M
List all available models
lemonade list
Hinweis: Dies ist die Prod-Version vom Jacob 24b-Sprachmodell, also die Version, die von Servern sofort für die Nutzung in der Cloud gepullt wird. Sie beinhaltet nur eine auf Q4 quantisierte GGUF-Datei. Das Haupt-Repository befindet sich hier.
Ich stelle vor: Jacob 24b, das Flagschiff-Sprachmodell im Stil von "Leichter Sprache"; neu, vielseitig und mehr als ausreichend für alles, was die Zielgruppe Leichter Sprache benötigt.
Es basiert auf Mistral Small 3.2 und wurde mit Hilfe von LoRA-Fine-Tuning auf einem synthetischen Datensatz von Gesprächen trainiert.
Das Modell...
- hat 24 Milliarden Parameter (davon ~92 Mio. als LoRA)
- ist in 16-Bit-Präzision verfügbar
- kann sowohl Text als auch Bilder verarbeiten (Multimodal)
- kann Tools aufrufen (Tool-Use), um externe Aufgaben zu erledigen
- nutzt das Mistral-Chat-Template
- ist optimiert für die Erzeugung von leicht verständlichem Text im Stil von "Leichter Sprache"
Trainingsdetails
Trainiert wurde das Modell für ca. 40 Minuten auf einer einzelnen NVIDIA RTX PRO 6000-GPU mit 96 GB VRAM.
Der genutzte Datensatz ist eine interne, synthetische Sammlung an Konversationen mit nach Qualität gefilterten und der Leichten Sprache stark angenäherten Texten. Es umfasst über 1k+ sich der Leichten Sprache annähernde Gespräche.
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4-bit
Model tree for jacob-ml/jacob-24b-prod
Base model
mistralai/Mistral-Small-3.1-24B-Base-2503