Instructions to use prithivMLmods/rnj-1-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/rnj-1-instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prithivMLmods/rnj-1-instruct-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/rnj-1-instruct-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/rnj-1-instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/rnj-1-instruct-GGUF", filename="rnj-1-instruct.BF16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use prithivMLmods/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 prithivMLmods/rnj-1-instruct-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/rnj-1-instruct-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/rnj-1-instruct-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/rnj-1-instruct-GGUF:BF16
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 prithivMLmods/rnj-1-instruct-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/rnj-1-instruct-GGUF:BF16
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 prithivMLmods/rnj-1-instruct-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/rnj-1-instruct-GGUF:BF16
Use Docker
docker model run hf.co/prithivMLmods/rnj-1-instruct-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/rnj-1-instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/rnj-1-instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/rnj-1-instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prithivMLmods/rnj-1-instruct-GGUF:BF16
- SGLang
How to use prithivMLmods/rnj-1-instruct-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/rnj-1-instruct-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/rnj-1-instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/rnj-1-instruct-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/rnj-1-instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use prithivMLmods/rnj-1-instruct-GGUF with Ollama:
ollama run hf.co/prithivMLmods/rnj-1-instruct-GGUF:BF16
- Unsloth Studio new
How to use prithivMLmods/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 prithivMLmods/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 prithivMLmods/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 prithivMLmods/rnj-1-instruct-GGUF to start chatting
- Pi new
How to use prithivMLmods/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 prithivMLmods/rnj-1-instruct-GGUF:BF16
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": "prithivMLmods/rnj-1-instruct-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/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 prithivMLmods/rnj-1-instruct-GGUF:BF16
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 prithivMLmods/rnj-1-instruct-GGUF:BF16
Run Hermes
hermes
- Docker Model Runner
How to use prithivMLmods/rnj-1-instruct-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/rnj-1-instruct-GGUF:BF16
- Lemonade
How to use prithivMLmods/rnj-1-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/rnj-1-instruct-GGUF:BF16
Run and chat with the model
lemonade run user.rnj-1-instruct-GGUF-BF16
List all available models
lemonade list
rnj-1-instruct-GGUF
rnj-1-instruct from EssentialAI is the instruction-tuned variant of the 8.3B-parameter dense language model family trained from scratch on 8.4T tokens using a Gemma 3-like architecture with global attention, YaRN for 32K context extension, 128K vocabulary, and Muon optimizer, delivering state-of-the-art open-weight performance in code generation (HumanEval+, MBPP+, BigCodeBench, LiveCodeBench v6), agentic coding (20.8% SWE-bench Verified bash-only, outperforming Gemini 2.0 Flash/Qwen2.5-Coder 32B), tool-calling (Berkeley Function Calling Leaderboard), multilingual code (MultiPL-E across C++/Java/JS/etc.), code infilling (86.21% HE-FIM-Python), math (GSM8k, Minerva-MATH, AIME '24/'25), and scientific reasoning (GPQA-Diamond, SuperGPQA) under Apache 2.0 license. Post-trained with limited SFT (150B tokens) for community extension, it supports pass@N scaling, FIM via special tokens, Hermes tool parser, and seamless integration with vLLM/SGLang (tool-choice enabled), Transformers (4.51.2+), llama.cpp quantization, Cline IDE agent, Claude Code router, and mini-SWE-agent for PR fixes, security patches, performance profiling (Enamel leader), and data visualization. Optimized for temperatures [0,0.6] with system prompts to mitigate code bias, it excels in real-world SWE trajectories but notes limitations in factual recall and identity hallucinations from web training data.
rnj-1-instruct [GGUF]
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| rnj-1-instruct.BF16.gguf | BF16 | 16.6 GB | Download |
| rnj-1-instruct.F16.gguf | F16 | 16.6 GB | Download |
| rnj-1-instruct.Q8_0.gguf | Q8_0 | 8.84 GB | Download |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
- Downloads last month
- 65
8-bit
16-bit
