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

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

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 commited on 1 day ago

Commit

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README.md +70 -5

README.md CHANGED Viewed

@@ -1,11 +1,76 @@
-# lexi-coder-v4.1
-Copyright (c) 2026 Reallexi LLC. All rights reserved.
-This is a standalone model export from training job #1472. Its selected adapter has been merged into the base weights, so no PEFT adapter is required at runtime.
-Base model: `microsoft/Phi-4-mini-instruct`
-Keep `reallexi-model.json`, `NOTICE`, and all applicable upstream license files with the model.
 Core: https://llm.reallexi.io

+---
+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