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

Libraries

How to use Hal0ai/FastContext-Hal0-4B-ROCmFP4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Hal0ai/FastContext-Hal0-4B-ROCmFP4",
	filename="FastContext-4B-ROCmFP4-STRIX_LEAN.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/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 Studio

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

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hal0ai/FastContext-Hal0-4B-ROCmFP4 to start chatting

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 @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/FastContext-Hal0-4B-ROCmFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new

OpenClaw new

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"

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

thinmint commited on 20 days ago

Commit

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1 Parent(s): 83aae9d

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+---
+license: mit
+base_model: microsoft/FastContext-1.0-4B-SFT
+base_model_relation: quantized
+pipeline_tag: text-generation
+library_name: gguf
+tags:
+- gguf
+- rocmfp4
+- quantized
+- amd
+- rocm
+- strix-halo
+- qwen3
+- agent
+- repository-exploration
+language:
+- en
+---
+<p align="center">
+  <img src="hal0-banner.png" alt="hal0" width="420"/>
+</p>
+# 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.
+Quantized and validated on **AMD Strix Halo** (Ryzen AI MAX+ 395 / Radeon 8060S, `gfx1151`)
+using [`hal0ai/amd-strix-halo-toolboxes`](https://github.com/hal0ai) 🛠️.
+> ### ⚠️ Read this first — special runtime required
+> This file uses the experimental **`Q4_0_ROCMFP4`** GGUF tensor format. It is **NOT** loadable by
+> stock `llama.cpp`, Ollama, LM Studio, or any standard GGUF runtime. It runs **only** in the
+> [`charlie12345/rocmfp4-llama`](https://github.com/charlie12345/rocmfp4-llama) fork.
+> ROCmFP4 is a custom Codebook10 / finite-UE4M3 layout — it is **not** MXFP4 or NVFP4.
+## What's in this repo
+| File | Size | Format | BPW |
+|---|---:|---|---:|
+| `FastContext-4B-ROCmFP4-STRIX_LEAN.gguf` | 2.05 GiB | `Q4_0_ROCMFP4_STRIX_LEAN` | 4.38 |
+`STRIX_LEAN` is a tensor-aware preset: norms stay `f32`, sensitive tensors keep higher precision,
+and the bulk of the weights use the dual/fast ROCmFP4 layouts.
+## Why ROCmFP4 here
+On Strix Halo, token generation is memory-bandwidth-bound, so 4-bit weights decode much faster than
+BF16 while keeping quality intact for tool-calling.
+### Performance (`llama-bench`, ROCm0, FlashAttention on, Radeon 8060S)
+| Metric | BF16 source | **ROCmFP4 STRIX_LEAN** | Δ |
+|---|---:|---:|---|
+| Size | 7.49 GiB | **2.05 GiB** | **3.65× smaller** |
+| Prefill `pp512` | 2388 t/s | 2244 t/s | ~same (compute-bound) |
+| Decode `tg128` | 25.6 t/s | **73.7 t/s** | **2.88× faster** |
+### Tool-calling quality (`server-test-function-call.py`, 5 multi-turn cases, greedy `temp 0`)
+| | BF16 source | ROCmFP4 STRIX_LEAN |
+|---|---:|---:|
+| Cases passed | 2/5 | 4/5 |
+In every case **both** models selected and ordered the correct tools — the only failures were
+"no final summary produced" after correct tool use, a stopping quirk shared by the BF16 source
+(not a quantization artifact). **Takeaway: FP4 introduced no measurable tool-calling regression.**
+A 5-case harness can't rank models finely, so read this as "quality preserved," not "FP4 > BF16."
+## How to run
+Build the fork for your AMD GPU (see its README), then:
+```bash
+HSA_OVERRIDE_GFX_VERSION=11.5.1 \
+GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
+./build-strix-rocmfp4/bin/llama-server \
+  -m FastContext-4B-ROCmFP4-STRIX_LEAN.gguf \
+  -dev ROCm0 -ngl 999 -c 262144 -fa on --jinja
+```
+For scripted/non-interactive generation use `llama-completion` (this fork's `llama-cli` is
+interactive-only and rejects `-no-cnv`). FastContext supports up to **262K** context.
+## How it was made
+```bash
+# 1. HF safetensors -> BF16 GGUF
+python convert_hf_to_gguf.py ./FastContext-1.0-4B-SFT --outtype bf16 --outfile fc-bf16.gguf
+# 2. BF16 -> ROCmFP4 (same fork binary the server uses)
+llama-quantize fc-bf16.gguf FastContext-4B-ROCmFP4-STRIX_LEAN.gguf Q4_0_ROCMFP4_STRIX_LEAN
+```
+## License & attribution
+- Weights derive from [`microsoft/FastContext-1.0-4B-SFT`](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT) — **MIT**.
+- Backbone: [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) — Apache-2.0.
+- Quantization format & tooling: [`charlie12345/rocmfp4-llama`](https://github.com/charlie12345/rocmfp4-llama).
+This repository redistributes a quantized derivative under the terms of the upstream MIT license.
+---
+### About hal0ai
+Built and benchmarked with **[hal0ai](https://github.com/hal0ai)** — local-first AI agent
+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. 👋