Instructions to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 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/FastContext-Hal0-4B-ROCmFP4
# Run inference directly in the terminal:
llama cli -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4
# Run inference directly in the terminal:
llama cli -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4

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/FastContext-Hal0-4B-ROCmFP4
# Run inference directly in the terminal:
./llama-cli -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4

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/FastContext-Hal0-4B-ROCmFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4

Use Docker

docker model run hf.co/Hal0ai/FastContext-Hal0-4B-ROCmFP4

LM Studio
Jan

vLLM

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hal0ai/FastContext-Hal0-4B-ROCmFP4"
# 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/FastContext-Hal0-4B-ROCmFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hal0ai/FastContext-Hal0-4B-ROCmFP4

Ollama
How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with Ollama:
```
ollama run hf.co/Hal0ai/FastContext-Hal0-4B-ROCmFP4
```
Unsloth Desktop

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4

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": "Hal0ai/FastContext-Hal0-4B-ROCmFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with Docker Model Runner:
```
docker model run hf.co/Hal0ai/FastContext-Hal0-4B-ROCmFP4
```

Lemonade

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hal0ai/FastContext-Hal0-4B-ROCmFP4

Run and chat with the model

lemonade run user.FastContext-Hal0-4B-ROCmFP4-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 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/FastContext-Hal0-4B-ROCmFP4

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/FastContext-Hal0-4B-ROCmFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hal0ai/FastContext-Hal0-4B-ROCmFP4

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/FastContext-Hal0-4B-ROCmFP4" \
  --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"

thinmint commited on Jun 24

Commit

61509a1

verified ·

1 Parent(s): eecf950

Add author note: first quant project, feedback welcome, hal0 slot

Browse files

Files changed (1) hide show

README.md +16 -2

README.md CHANGED Viewed

@@ -24,8 +24,6 @@ language:
 # FastContext-Hal0-4B — ROCmFP4 (STRIX_LEAN)
-### **73.7 t/s** | **2.88× faster** | **3.65× smaller**
 A 4-bit **ROCmFP4** quantization of [`microsoft/FastContext-1.0-4B-SFT`](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT),
 a lightweight repository-exploration subagent (Qwen3-4B backbone) for LLM coding agents.
@@ -112,3 +110,19 @@ infrastructure tuned for **AMD Strix Halo**. The
 [`amd-strix-halo-toolboxes`](https://github.com/hal0ai) ship ready-to-run ROCm + ROCmFP4
 container images so you can quantize and serve large models on a single unified-memory APU.
 If you're running agents on AMD silicon, come say hi. 👋

 # FastContext-Hal0-4B — ROCmFP4 (STRIX_LEAN)
 A 4-bit **ROCmFP4** quantization of [`microsoft/FastContext-1.0-4B-SFT`](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT),
 a lightweight repository-exploration subagent (Qwen3-4B backbone) for LLM coding agents.
 [`amd-strix-halo-toolboxes`](https://github.com/hal0ai) ship ready-to-run ROCm + ROCmFP4
 container images so you can quantize and serve large models on a single unified-memory APU.
 If you're running agents on AMD silicon, come say hi. 👋
+---
+### A note from the author 🙇
+This is my **first time** doing any kind of custom model quantization or training — this
+release is very much a learning project. So if you spot something I got wrong, or have tips on
+presets, calibration, or quality testing, I'd genuinely **appreciate the feedback** — open a
+Community discussion and let me know.
+I made this to run as a **slot in [hal0](https://github.com/hal0ai)**, alongside the main agent —
+a small, fast repository-exploration subagent that ROCmFP4 lets me keep resident on the Strix
+Halo without crowding out the bigger models sharing the same unified memory.
+If you're tinkering with local agents on AMD hardware, **come check out hal0** — would love to
+see what you build. 🙂