Instructions to use BloxyCola/loramax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use BloxyCola/loramax 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 BloxyCola/loramax:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BloxyCola/loramax:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BloxyCola/loramax:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BloxyCola/loramax: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 BloxyCola/loramax:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BloxyCola/loramax: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 BloxyCola/loramax:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BloxyCola/loramax:Q4_K_M

Use Docker

docker model run hf.co/BloxyCola/loramax:Q4_K_M

LM Studio
Jan
Ollama
How to use BloxyCola/loramax with Ollama:
```
ollama run hf.co/BloxyCola/loramax:Q4_K_M
```
Unsloth Desktop

How to use BloxyCola/loramax with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BloxyCola/loramax:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "BloxyCola/loramax:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use BloxyCola/loramax with Docker Model Runner:
```
docker model run hf.co/BloxyCola/loramax:Q4_K_M
```

Lemonade

How to use BloxyCola/loramax with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BloxyCola/loramax:Q4_K_M

Run and chat with the model

lemonade run user.loramax-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use BloxyCola/loramax with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BloxyCola/loramax: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 BloxyCola/loramax:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use BloxyCola/loramax with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BloxyCola/loramax: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 "BloxyCola/loramax: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"

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Check out the documentation for more information.

Lora Max

Lora Max is a Mistral-based AI assistant configured for accuracy, practical assistance, reasoning, programming, and general conversation.

Benchmark

Inference — Apple M1 Pro 16GB

Metric	Result
Generation speed	22.25 tok/s
Prompt processing	14,085.83 tok/s
Prompt tokens	2,847
Output tokens	252
Total duration	11.565 s
Load duration	4.424 ms

Evaluation

Benchmark	Score
MMLU	TBD
GPQA Diamond	TBD
GSM8K	TBD
HumanEval	TBD
TruthfulQA	TBD

Base Model

Mistral

Hardware

Apple M1 Pro 16 GB RAM

Ollama

ollama create lora-max -f Modelfile
ollama run lora-max

Downloads last month: 4

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

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