Instructions to use reallexi/lexi-coder-8b-v1 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-8b-v1 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-8b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reallexi/lexi-coder-8b-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reallexi/lexi-coder-8b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reallexi/lexi-coder-8b-v1: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 reallexi/lexi-coder-8b-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf reallexi/lexi-coder-8b-v1: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 reallexi/lexi-coder-8b-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reallexi/lexi-coder-8b-v1:Q4_K_M

Use Docker

docker model run hf.co/reallexi/lexi-coder-8b-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use reallexi/lexi-coder-8b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reallexi/lexi-coder-8b-v1"
# 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-8b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reallexi/lexi-coder-8b-v1:Q4_K_M

Ollama
How to use reallexi/lexi-coder-8b-v1 with Ollama:
```
ollama run hf.co/reallexi/lexi-coder-8b-v1:Q4_K_M
```

Unsloth Studio

How to use reallexi/lexi-coder-8b-v1 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-8b-v1 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-8b-v1 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-8b-v1 to start chatting

How to use reallexi/lexi-coder-8b-v1 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-8b-v1: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": "reallexi/lexi-coder-8b-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use reallexi/lexi-coder-8b-v1 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-8b-v1: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 reallexi/lexi-coder-8b-v1:Q4_K_M

Run Hermes

hermes

OpenClaw new

How to use reallexi/lexi-coder-8b-v1 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-8b-v1:Q4_K_M

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-8b-v1:Q4_K_M" \
  --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-8b-v1 with Docker Model Runner:
```
docker model run hf.co/reallexi/lexi-coder-8b-v1:Q4_K_M
```

Lemonade

How to use reallexi/lexi-coder-8b-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull reallexi/lexi-coder-8b-v1:Q4_K_M

Run and chat with the model

lemonade run user.lexi-coder-8b-v1-Q4_K_M

List all available models

lemonade list

Atomic Chat

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reallexi/lexi-coder-8b-v1

A GGUF build of 8.04B parameters, derived from meta-llama/Llama-3.1-8B-Instruct.

Size and requirements


Parameters	8,037,076,992 (8.04B)
Weights on disk	4.58 GB
Quantization	Q4_K_M
Trained context length	2,048 tokens
Base model	`meta-llama/Llama-3.1-8B-Instruct`

Approximate memory to hold the weights. Add context and runtime overhead on top.

Precision	Weights
FP16 / BF16	14.97 GB
8-bit (Q8_0)	7.49 GB
4-bit (Q4_K_M)	4.12 GB

Training


Strategy	llm
Adapter	Auto LoRA
LoRA rank / alpha	8 / 16
Dataset	`TIGER-Lab/MathInstruct`
Samples learned	1,000 (through phase 8 of 15)
Training steps	1,250
Epochs	5

Usage

llama-cli -m reallexi/lexi-coder-8b-v1.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: meta-llama/Llama-3.1-8B-Instruct
Training data: TIGER-Lab/MathInstruct

Produced by Reallexi LLC AI Model Builder from training job #1297. Core: https://llm.reallexi.io

Downloads last month: 64

Safetensors

Model size

8B params

Tensor type

F16

Model tree for reallexi/lexi-coder-8b-v1

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(893)

this model