Instructions to use Hal0ai/hal0-brain-sft-fpx8-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hal0ai/hal0-brain-sft-fpx8-agent with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Hal0ai/hal0-brain-sft-fpx8-agent",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Hal0ai/hal0-brain-sft-fpx8-agent 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 Hal0ai/hal0-brain-sft-fpx8-agent
# Run inference directly in the terminal:
llama cli -hf Hal0ai/hal0-brain-sft-fpx8-agent

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hal0ai/hal0-brain-sft-fpx8-agent
# Run inference directly in the terminal:
llama cli -hf Hal0ai/hal0-brain-sft-fpx8-agent

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 Hal0ai/hal0-brain-sft-fpx8-agent
# Run inference directly in the terminal:
./llama-cli -hf Hal0ai/hal0-brain-sft-fpx8-agent

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 Hal0ai/hal0-brain-sft-fpx8-agent
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hal0ai/hal0-brain-sft-fpx8-agent

Use Docker

docker model run hf.co/Hal0ai/hal0-brain-sft-fpx8-agent

LM Studio
Jan

vLLM

How to use Hal0ai/hal0-brain-sft-fpx8-agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hal0ai/hal0-brain-sft-fpx8-agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hal0ai/hal0-brain-sft-fpx8-agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hal0ai/hal0-brain-sft-fpx8-agent

Ollama
How to use Hal0ai/hal0-brain-sft-fpx8-agent with Ollama:
```
ollama run hf.co/Hal0ai/hal0-brain-sft-fpx8-agent
```

Unsloth Studio

How to use Hal0ai/hal0-brain-sft-fpx8-agent 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 Hal0ai/hal0-brain-sft-fpx8-agent 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 Hal0ai/hal0-brain-sft-fpx8-agent to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hal0ai/hal0-brain-sft-fpx8-agent to start chatting

How to use Hal0ai/hal0-brain-sft-fpx8-agent with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hal0ai/hal0-brain-sft-fpx8-agent

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": "Hal0ai/hal0-brain-sft-fpx8-agent"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Hal0ai/hal0-brain-sft-fpx8-agent with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hal0ai/hal0-brain-sft-fpx8-agent

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 Hal0ai/hal0-brain-sft-fpx8-agent

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Hal0ai/hal0-brain-sft-fpx8-agent with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hal0ai/hal0-brain-sft-fpx8-agent

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 "Hal0ai/hal0-brain-sft-fpx8-agent" \
  --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 Hal0ai/hal0-brain-sft-fpx8-agent with Docker Model Runner:
```
docker model run hf.co/Hal0ai/hal0-brain-sft-fpx8-agent
```

Lemonade

How to use Hal0ai/hal0-brain-sft-fpx8-agent with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hal0ai/hal0-brain-sft-fpx8-agent

Run and chat with the model

lemonade run user.hal0-brain-sft-fpx8-agent-{{QUANT_TAG}}

List all available models

lemonade list

hal0-brain ROCmFP8 Agent

ROCmFPX ROCmFP8 Agent quantization of Hal0ai/hal0-brain-sft for hal0's brain role.

This is not a stock Q8_0 GGUF. It contains the custom Q8_0_ROCMFPX tensor type (GGML type ID 103) and requires a ROCmFPX-capable runner. Stock llama.cpp, Ollama, LM Studio, and other upstream-only GGUF runners must reject this file rather than attempt inference.

Artifact contract

Field	Value
File	`model.gguf`
ROCmFPX preset	`Q8_0_ROCMFPX_AGENT`
Catalog format	`rocmfpx-gguf`
Size	`1,144,682,016` bytes
SHA-256	`eeb38e45877d26a5923d2ec22e1c887a0b12f0c40f9df72015bd73f11d1263e4`
Architecture	Llama, 1.1B parameters
Intended role	hal0 brain / tool-use agent
License	Apache-2.0

ROCmFPX-aware inspection reports 219 tensors:

58 × Q8_0_ROCMFPX (custom type ID 103)
112 × Q8_0 (type ID 8)
49 × F32 (type ID 0)

The mixed tensor policy is expected for the Agent preset: it keeps selected agent-coherence-sensitive tensors on protected types while using ROCmFPX FP8 weights elsewhere. general.file_type is 115 (Q8_0_ROCMFPX_AGENT).

Required runner

Use only a runner explicitly declaring rocmfpx-gguf compatibility. The hal0 catalog allows this model only with its rocmfpx and vulkanfpx runners.

Known compatible hal0 runner:

ghcr.io/hal0ai/hal0-rocmfpx@sha256:fd6b02a720e633e402e929e19eedefff52aeec18e5de8f43e525689e523985f3

That image provides ROCm/HIP and Vulkan backends and includes MiniCPM5 tokenizer support needed by this model. Do not substitute a stock llama.cpp image.

Typical direct invocation inside a compatible image is:

llama-server \
  --model /models/model.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  -ngl 999 \
  -fa on

Select ROCm0 or Vulkan0 according to the host and runner configuration.

Provenance

The artifact was generated from the immutable F16 GGUF:

Repository: Hal0ai/hal0-brain-sft-GGUF
Revision:   6b190df6e816cc806f7fa7ae3de7248f5551e00b
File:       model.gguf
SHA-256:    ed9d28c4eac1d7c291bc80d9410c243a3d28e655921ccaf90f2b6619aa24d2c3

Quantization used charlie12345/ROCmFPX commit 61f2f2d7bc4955e9bca821095ef69125837133b5 and its agent wrapper:

SRC=/path/to/model-F16.gguf \
OUT=/path/to/model.gguf \
FORMAT=rocmfp8 \
PROFILE=agent \
scripts/quantize-rocmfpx-agent.sh

The resolved preset was Q8_0_ROCMFPX_AGENT.

Verification

The published digest was calculated over the complete file. Tensor types were read with the ROCmFPX fork's gguf-py reader, not inferred from repository or filename labels.

A current stock llama.cpp build was also tested as a negative compatibility check and rejected the file at the first custom tensor:

tensor 'blk.3.attn_k.weight' has invalid ggml type 103

This rejection is expected and required.

Downloads last month: 34

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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