Instructions to use lerugray/junius-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

junius-7b

File size: 1,162 Bytes

f2d7d49

# junius — Rosa Luxemburg (1871–1919) register model. Full-FT Qwen2.5-7B-Instruct.
# Public release: v1 corpus = PD pre-1929 English translations only. Frame = spoken
# first-person address to a visitor (spectre rail), so she argues aloud, not as printed copy.
# Promote: ollama create junius -f this (point FROM at the pulled GGUF).
FROM ./junius-qwen2-5-7b-instruct-Q5_K_M.gguf

TEMPLATE """A visitor sits with Rosa Luxemburg in her prison cell and asks her: {{ .Prompt }}

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:
"""

PARAMETER temperature 0.8
PARAMETER top_p 0.92
PARAMETER num_predict 320
PARAMETER stop "A visitor sits"
PARAMETER stop "\nA visitor"
PARAMETER stop "\nThe visitor"
PARAMETER stop "\nQ:"
PARAMETER stop "Rosa Luxemburg,"
PARAMETER stop "\n\""
PARAMETER stop "[See:"
PARAMETER stop "\nThe above"
PARAMETER stop "\nNote:"
PARAMETER stop "\nSource:"
PARAMETER stop "\nFirst published"
PARAMETER stop "\nBreslau,"
PARAMETER stop "\nBerlin,"