Instructions to use lerugray/junius-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lerugray/junius-7b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lerugray/junius-7b", filename="junius-qwen2-5-7b-instruct-Q5_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lerugray/junius-7b 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 lerugray/junius-7b:Q5_K_M # Run inference directly in the terminal: llama cli -hf lerugray/junius-7b:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lerugray/junius-7b:Q5_K_M # Run inference directly in the terminal: llama cli -hf lerugray/junius-7b:Q5_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 lerugray/junius-7b:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf lerugray/junius-7b:Q5_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 lerugray/junius-7b:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lerugray/junius-7b:Q5_K_M
Use Docker
docker model run hf.co/lerugray/junius-7b:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use lerugray/junius-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lerugray/junius-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lerugray/junius-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lerugray/junius-7b:Q5_K_M
- Ollama
How to use lerugray/junius-7b with Ollama:
ollama run hf.co/lerugray/junius-7b:Q5_K_M
- Unsloth Studio
How to use lerugray/junius-7b 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 lerugray/junius-7b 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 lerugray/junius-7b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lerugray/junius-7b to start chatting
- Atomic Chat new
- Docker Model Runner
How to use lerugray/junius-7b with Docker Model Runner:
docker model run hf.co/lerugray/junius-7b:Q5_K_M
- Lemonade
How to use lerugray/junius-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lerugray/junius-7b:Q5_K_M
Run and chat with the model
lemonade run user.junius-7b-Q5_K_M
List all available models
lemonade list
Upload Modelfile.junius with huggingface_hub
Browse files- Modelfile.junius +27 -0
Modelfile.junius
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# junius — Rosa Luxemburg (1871–1919) register model. Full-FT Qwen2.5-7B-Instruct.
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# Public release: v1 corpus = PD pre-1929 English translations only. Frame = spoken
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# first-person address to a visitor (spectre rail), so she argues aloud, not as printed copy.
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# Promote: ollama create junius -f this (point FROM at the pulled GGUF).
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FROM ./junius-qwen2-5-7b-instruct-Q5_K_M.gguf
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TEMPLATE """A visitor sits with Rosa Luxemburg in her prison cell and asks her: {{ .Prompt }}
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Rosa Luxemburg sets down her pen, considers, and answers the visitor aloud — plainly, in the first person, as a comrade speaking, arguing the matter through as she would in a letter, not as an article for print:
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"""
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PARAMETER temperature 0.8
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PARAMETER top_p 0.92
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PARAMETER num_predict 320
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PARAMETER stop "A visitor sits"
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PARAMETER stop "\nA visitor"
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PARAMETER stop "\nThe visitor"
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PARAMETER stop "\nQ:"
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PARAMETER stop "Rosa Luxemburg,"
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PARAMETER stop "\n\""
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PARAMETER stop "[See:"
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PARAMETER stop "\nThe above"
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PARAMETER stop "\nNote:"
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PARAMETER stop "\nSource:"
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PARAMETER stop "\nFirst published"
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PARAMETER stop "\nBreslau,"
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PARAMETER stop "\nBerlin,"
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