Instructions to use reallexi/lexi-coder-v4.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use reallexi/lexi-coder-v4.1 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 reallexi/lexi-coder-v4.1:F16 # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v4.1:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf reallexi/lexi-coder-v4.1:F16 # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v4.1:F16
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 reallexi/lexi-coder-v4.1:F16 # Run inference directly in the terminal: ./llama-cli -hf reallexi/lexi-coder-v4.1:F16
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 reallexi/lexi-coder-v4.1:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf reallexi/lexi-coder-v4.1:F16
Use Docker
docker model run hf.co/reallexi/lexi-coder-v4.1:F16
- LM Studio
- Jan
- vLLM
How to use reallexi/lexi-coder-v4.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reallexi/lexi-coder-v4.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reallexi/lexi-coder-v4.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/reallexi/lexi-coder-v4.1:F16
- Ollama
How to use reallexi/lexi-coder-v4.1 with Ollama:
ollama run hf.co/reallexi/lexi-coder-v4.1:F16
- Unsloth Studio
How to use reallexi/lexi-coder-v4.1 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 reallexi/lexi-coder-v4.1 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 reallexi/lexi-coder-v4.1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for reallexi/lexi-coder-v4.1 to start chatting
- Pi
How to use reallexi/lexi-coder-v4.1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v4.1:F16
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": "reallexi/lexi-coder-v4.1:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use reallexi/lexi-coder-v4.1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v4.1:F16
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 reallexi/lexi-coder-v4.1:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use reallexi/lexi-coder-v4.1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v4.1:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "reallexi/lexi-coder-v4.1:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use reallexi/lexi-coder-v4.1 with Docker Model Runner:
docker model run hf.co/reallexi/lexi-coder-v4.1:F16
- Lemonade
How to use reallexi/lexi-coder-v4.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reallexi/lexi-coder-v4.1:F16
Run and chat with the model
lemonade run user.lexi-coder-v4.1-F16
List all available models
lemonade list
reallexi/lexi-coder-v4.1
A GGUF build of 3.85B parameters, derived from microsoft/Phi-4-mini-instruct.
Size and requirements
| Parameters | 3,847,556,096 (3.85B) |
| Weights on disk | 9.48 GB |
| Quantization | Q4_K_M |
| Trained context length | 1,024 tokens |
| Base model | microsoft/Phi-4-mini-instruct |
Approximate memory to hold the weights. Add context and runtime overhead on top.
| Precision | Weights |
|---|---|
| FP16 / BF16 | 7.17 GB |
| 8-bit (Q8_0) | 3.58 GB |
| 4-bit (Q4_K_M) | 1.97 GB |
Training
| Strategy | lora |
| Adapter | Auto LoRA |
| LoRA rank / alpha | 8 / 16 |
| Dataset | ianncity/GLM-5.2-Conversation |
| Samples learned | 50,296 (through phase 11 of 20) |
| Training steps | 370 |
| Epochs | 5 |
Usage
llama-cli -m reallexi/lexi-coder-v4.1.gguf -p "Your prompt here"
License and attribution
The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.
Base model:
microsoft/Phi-4-mini-instructTraining data:
ianncity/GLM-5.2-Conversation
Copyright (c) 2026 Reallexi LLC. All rights reserved.
Produced by Reallexi LLC AI Model Builder from training job #1472. Core: https://llm.reallexi.io
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