Transformers
GGUF
English
German
German
RAG
Retrieval
Question-Answering
Summarization
Reasoning
conversational
Instructions to use mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-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 mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF: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 mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF: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 mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF with Ollama:
ollama run hf.co/mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF / German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI.Q3_K_M.gguf
Download German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI.Q3_K_M.gguf from mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.88 GB
-
https://huggingface.co/mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF/resolve/main/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI.Q3_K_M.gguf
- Command line
-
hf download hf://mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI.Q3_K_M.gguf
-
curl -L -o German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI.Q3_K_M.gguf https://huggingface.co/mradermacher/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI-GGUF/resolve/main/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI.Q3_K_M.gguf
1.88 GB
- Xet hash:
- ff6b480e0c954cfa51210a6650db4aa2de7b490d9388a4e2d872801556ce9ce8
- Size of remote file:
- 1.88 GB
- SHA256:
- 895b33761b663dbc9d4f3f8a7d9ca03fd007a248778db04b27efdaeca6f143ac
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