Text Generation
GGUF
Safetensors
phi3
ai-model-builder
fine-tuned
llama.cpp
lora
q4_k_m
reallexi
conversational
custom_code
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
- 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
- Atomic Chat
| license: other | |
| license_name: "inherits-base-model-and-dataset-terms" | |
| base_model: "microsoft/Phi-4-mini-instruct" | |
| library_name: gguf | |
| pipeline_tag: "text-generation" | |
| tags: | |
| - "ai-model-builder" | |
| - "fine-tuned" | |
| - gguf | |
| - llama.cpp | |
| - lora | |
| - q4_k_m | |
| - reallexi | |
| - "text-generation" | |
| # reallexi/lexi-coder-v4.1 | |
| A GGUF build of 3.85B parameters, derived from [`microsoft/Phi-4-mini-instruct`](https://huggingface.co/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 | |
| ```bash | |
| 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-instruct`](https://huggingface.co/microsoft/Phi-4-mini-instruct) | |
| - Training 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 | |