Instructions to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST 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 alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

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 alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

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 alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

Use Docker

docker model run hf.co/alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

LM Studio
Jan
Ollama
How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with Ollama:
```
ollama run hf.co/alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP
```
Unsloth Desktop

How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

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": "alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with Docker Model Runner:
```
docker model run hf.co/alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP
```

Lemonade

How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-ROCMFP4_FAST-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

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 alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP

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 "alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST:Q4_0_ROCMFP" \
  --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"

Llama-3.1-8B-Instruct-ROCMFP4_FAST - ROCmFPX Quantized

This model was dynamically quantized and generated using the ROCmFPX My Repo Space

Original Base Model: meta-llama/Llama-3.1-8B-Instruct

Usage Instructions with ROCmFPX llama.cpp

To run this model, you must use the specialized ROCmFPX fork of llama.cpp by charlie12345. Standard llama.cpp releases do not currently support ROCmFPX quantization formats.

# 1. Clone the ROCmFPX repository
git clone --depth 1 [https://github.com/charlie12345/ROCmFPX.git](https://github.com/charlie12345/ROCmFPX.git)
cd ROCmFPX

# 2. Build the project
cmake -B build -G Ninja -DGGML_CUDA=OFF -DGGML_NATIVE=OFF
cmake --build build --target llama-cli -j

# 3. Run the model
./build/bin/llama-cli -m Llama-3.1-8B-Instruct-Q4_0_ROCMFP4_FAST.gguf -p "You are a helpful assistant. Hello!" -n 128

License

This model is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Please adhere to the original license constraints of the base model and provide appropriate attribution when sharing.

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Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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Model tree for alst10/Llama-3.1-8B-Instruct-ROCMFP4_FAST

Base model

meta-llama/Llama-3.1-8B

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

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alst10
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Llama-3.1-8B-Instruct-ROCMFP4_FAST

Llama-3.1-8B-Instruct-ROCMFP4_FAST - ROCmFPX Quantized

Usage Instructions with ROCmFPX llama.cpp

License

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