Instructions to use EssentialAI/rnj-1-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use EssentialAI/rnj-1-instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="EssentialAI/rnj-1-instruct-GGUF", filename="Rnj-1-Instruct-8B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use EssentialAI/rnj-1-instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf EssentialAI/rnj-1-instruct-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 EssentialAI/rnj-1-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use EssentialAI/rnj-1-instruct-GGUF with Ollama:
ollama run hf.co/EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use EssentialAI/rnj-1-instruct-GGUF 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 EssentialAI/rnj-1-instruct-GGUF 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 EssentialAI/rnj-1-instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EssentialAI/rnj-1-instruct-GGUF to start chatting
- Pi
How to use EssentialAI/rnj-1-instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf EssentialAI/rnj-1-instruct-GGUF: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": "EssentialAI/rnj-1-instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use EssentialAI/rnj-1-instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf EssentialAI/rnj-1-instruct-GGUF: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 EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use EssentialAI/rnj-1-instruct-GGUF with Docker Model Runner:
docker model run hf.co/EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
- Lemonade
How to use EssentialAI/rnj-1-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull EssentialAI/rnj-1-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.rnj-1-instruct-GGUF-Q4_K_M
List all available models
lemonade list
Philip Monk commited on
Commit ·
56f262a
1
Parent(s): 4ebff14
update readme to reflect llama.cpp and ollama released support
Browse files
README.md
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---
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This is a GGUF-formatted checkpoint of
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[rnj-1-instruct](https://huggingface.co/EssentialAI/rnj-1-instruct) suitable
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for use in llama.cpp. This has been quantized with the
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results in model weights of size 4.8GB.
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build from source with these instructions for MacOS. For Linux, install cmake
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using your package manager. For Windows, consult the llama.cpp [build
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guide](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md).
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```bash
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cd llama.cpp
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git checkout rnj-1
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cmake -B build
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cmake --build build --config Release
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```
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```
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```
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To run it in the CLI, use this command:
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```
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build/bin/llama-cli -hf EssentialAI/rnj-1-instruct-GGUF
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```
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---
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This is a GGUF-formatted checkpoint of
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[rnj-1-instruct](https://huggingface.co/EssentialAI/rnj-1-instruct) suitable
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for use in llama.cpp, Ollama, or others. This has been quantized with the
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Q4\_K\_M scheme, which results in model weights of size 4.8GB.
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For llama.cpp, install (after version 7328) and run either of these commands:
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```bash
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llama-cli -hf EssentialAI/rnj-1-instruct-GGUF
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llama-server -hf EssentialAI/rnj-1-instruct-GGUF -c 0 # and open browser to localhost:8080
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```
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For Ollama, install (after version v0.13.3) and run:
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```bash
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ollama run rnj-1
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```
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