Instructions to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF 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 kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF: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 kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF: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 kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with Ollama:
```
ollama run hf.co/kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF 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 kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF 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 kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF to start chatting

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

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": "kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.BTL-4-ROCmFP4-STRIX_LEAN-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF: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 kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF: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 "kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF: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"

BTL-4 — ROCmFP4 STRIX_LEAN (GGUF) — AMD Ryzen AI Max+ 395 / Strix Halo / gfx1151

ROCmFP4 quantization of badtheorylabs/BTL-4, built for AMD Strix Halo (gfx1151) with the ROCmFPX fork of llama.cpp.

BTL-4 is a 70.2 GB bf16 MoE vision model — Qwen3_5MoeForConditionalGeneration, 40 layers, 256 experts / 8 active, hidden 2048, vocab 248320. An mmproj is included and is required for image input.

Measured results (Ryzen AI MAX+ 395, 124 GB, gfx1151)

build	size	real BPW	decode	quality /10
`Q4_0_ROCMFP4_STRIX_LEAN`	18,597,336,928 B (17.32 GiB)	4.29	+12%	9
`Q4_K_M` (control, run twice)	21,166,757,728 B (19.71 GiB)	4.88	baseline	8 / 8

LEAN is −12% on disk and +12% faster than Q4_K_M, at equal-or-better quality. Quality used greedy decoding with the baseline run twice to establish the noise floor (Δ=0 between passes).

A sister build, Q4_0_ROCMFP4_STRIX (4.31 BPW, 17.39 GiB), was produced independently on a second box and measured +11.4% over Q4_K_M at 10/10 = 10/10 quality. The two tiers are within ~67 MB and ~0.02 BPW of each other; prior art on this architecture family favours plain STRIX for quality-critical work.

⚠️ Q3 is a trap on this architecture

tier	advertised BPW	real BPW
`STRIX_LEAN` (106)	~4.38	4.29
`Q3_0_ROCMFPX`	3.50	4.45
`Q4_K_M`	~4.58	4.88

Q3 quantizes larger than LEAN. Always run --dry-run and read the real BPW — the advertised value is not reliable.

⛔ No speculative decoding

Upstream BTL-4 ships mtp_num_hidden_layers: 0 — there are no MTP tensors. Do not pass a speculative/MTP flag. A spec flag with no draft tensors loads and reports READY while producing garbage.

Usage

export HSA_OVERRIDE_GFX_VERSION=11.5.1
export GGML_HIP_ENABLE_UNIFIED_MEMORY=1

llama-server \
  -m BTL-4-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
  --mmproj mmproj-BTL-4-f16.gguf \
  -ngl 999 -fa on -dio --no-warmup --jinja \
  -c 32768 --parallel 1

Requires a ROCmFP4-capable llama.cpp build; stock llama.cpp will not load these tensor types.

License

Inherits the licence of the base model, badtheorylabs/BTL-4.

Other public builds of this model

Compiled from Hugging Face repository metadata — file sizes, shipped files, quant variant as named by each repo. No third-party build was run or benchmarked here, so this table makes no speed or quality claim about any of them. It is here so you can see the size and format options at a glance and pick what fits your hardware.

Repository	Largest model file	Variant	Ships	Downloads	Likes
`kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF` (this repo)	17.32 GiB	STRIX_LEAN	vision	102	0
`kingjones777/BTL-4-ROCmFP4-STRIX-GGUF`	17.38 GiB	STRIX	vision	77	0

Base model: badtheorylabs/BTL-4. Generated from Hub metadata; download counts move over time.

Acknowledgements

This build would not exist without the work below. Please star and follow these projects — the quantisation format used here is their engineering, not mine.

ROCmFPX — maintained by charlie12345 / caf The ROCmFP4 / ROCmFPX tensor formats (ggml types 100–106) exist only in this fork. Every ROCmFP4 file in this repository was produced with its llama-quantize, and runs on its runtime. The fork also credits collaborators ciru-ai, Tom Turney, PlunderStruck and Aydan S., and acknowledges AMD for hardware support. Licensed MIT, based on upstream llama.cpp.

llama.cpp — ggml-org and contributors The inference engine, GGUF format and conversion tooling everything here is built on.

AMD ROCm The compute platform these builds target — ROCm 7.2.4 on gfx1151 / Radeon 8060S.

Base model authors — see base_model in the metadata above; all model weights, licences and capabilities are theirs. This repository contributes quantisation and measurement only.

If you use these files, please credit ROCmFPX alongside this repository.

Downloads last month: 278

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF

Base model

badtheorylabs/BTL-4

Quantized

(10)

this model