Instructions to use SafeAgent/safeagent-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use SafeAgent/safeagent-7b-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SafeAgent/safeagent-7b-v1", filename="safeagent-7b-v1.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use SafeAgent/safeagent-7b-v1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SafeAgent/safeagent-7b-v1 # Run inference directly in the terminal: llama-cli -hf SafeAgent/safeagent-7b-v1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SafeAgent/safeagent-7b-v1 # Run inference directly in the terminal: llama-cli -hf SafeAgent/safeagent-7b-v1
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 SafeAgent/safeagent-7b-v1 # Run inference directly in the terminal: ./llama-cli -hf SafeAgent/safeagent-7b-v1
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 SafeAgent/safeagent-7b-v1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf SafeAgent/safeagent-7b-v1
Use Docker
docker model run hf.co/SafeAgent/safeagent-7b-v1
- LM Studio
- Jan
- Ollama
How to use SafeAgent/safeagent-7b-v1 with Ollama:
ollama run hf.co/SafeAgent/safeagent-7b-v1
- Unsloth Studio new
How to use SafeAgent/safeagent-7b-v1 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 SafeAgent/safeagent-7b-v1 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 SafeAgent/safeagent-7b-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SafeAgent/safeagent-7b-v1 to start chatting
- Docker Model Runner
How to use SafeAgent/safeagent-7b-v1 with Docker Model Runner:
docker model run hf.co/SafeAgent/safeagent-7b-v1
- Lemonade
How to use SafeAgent/safeagent-7b-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SafeAgent/safeagent-7b-v1
Run and chat with the model
lemonade run user.safeagent-7b-v1-{{QUANT_TAG}}List all available models
lemonade list
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)SafeAgent 7B v1
The AI model powering SafeAgent ? your personal AI agent that runs entirely on your machine.
SafeAgent lets you chat with AI, read your emails, search the web, manage GitHub, post to Slack, and automate workflows ? all running locally. Your data never leaves your network. No subscriptions. No cloud dependency.
Run It Now
ollama run SafeAgent/safeagent-7b-v1
What SafeAgent Can Do With This Model
- Read and send emails (Gmail, Outlook, Yahoo)
- Search the web and summarise results
- Write, fix, and explain code
- Plan trips, book restaurants, compare products
- Automate workflows and scheduled tasks
- Everything stays on your machine -- AES-256 encrypted
Install SafeAgent
curl -sL https://www.safeagent.dev/docker-compose.yml -o docker-compose.yml && docker compose up
Open http://localhost:3000 -- running in 30 seconds.
Model Details
| Property | Value |
|---|---|
| Base model | Mistral 7B v0.1 |
| Fine-tuning method | QLoRA 4-bit |
| Training data | 80,000 OpenOrca instruction examples |
| Training time | 20 hours |
| Final loss | 0.5564 |
| Format | GGUF f16 |
Why Local AI?
| Cloud AI | SafeAgent |
|---|---|
| Your data on their servers | Everything on your machine |
| $20-100/month | Free forever |
| Rate limited | No limits -- your hardware |
| Vendor lock-in | Open source, always |
| They train on your data | Your data stays yours |
Links
- Website: https://safeagent.dev
- DockerHub: https://hub.docker.com/u/gurmukhs
- Built by Gurmukh Singh: https://www.linkedin.com/in/gurmukh-singh-38a1a4246/
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Hardware compatibility
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Inference Providers NEW
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SafeAgent/safeagent-7b-v1", filename="safeagent-7b-v1.gguf", )