Instructions to use kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-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/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF to start chatting

How to use kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF: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": "kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-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/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Granite-4.2-3B-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/Granite-4.2-3B-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/Granite-4.2-3B-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"

Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF / README.md

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	---
	license: apache-2.0
	base_model: ibm-granite/granite-4.2-3b
	base_model_relation: quantized
	pipeline_tag: text-generation
	library_name: gguf
	tags: [gguf, llama.cpp, rocm, amd, strix-halo, gfx1151, ryzen-ai-max-395, rocmfp4, rocmfpx, strix-lean, granite, quantized]
	---

	# Granite 4.2-3B (STRIX_LEAN) — ROCmFP4 for AMD Strix Halo (gfx1151)

	I built this STRIX_LEAN quantization of ibm-granite/granite-4.2-3b on my Strix Halo box
	for the ROCmFPX runtime. This is the 4th tier of my Granite 4.2 set — the lean 4-bit one
	people normally want.

	## The file

	\| \| \|
	\|---\|---\|
	\| ftype \| `106` — `Q4_0_ROCMFP4_STRIX_LEAN` \|
	\| size \| 2,066,204,736 bytes (1.92 GiB) \|
	\| bpw \| 4.51 \|
	\| architecture \| `granite` \|
	\| tensors \| 363 \|
	\| context \| 131,072 \|
	\| token embedding \| Q5_K (the LEAN part) \|
	\| `output.weight` \| Q6_K (protected) \|
	\| sha256 \| `72c0e6361a3c71c0d917d04b6cd576cebedabbe3f1799a3542fdcde6a3987c99` \|

	Type histogram, read from the finished file:

	```
	Q4_0_ROCMFP4_FAST x200, F32 x81, Q4_0_ROCMFP4 x80, Q6_K x1, Q5_K x1
	```

	## What STRIX_LEAN is — and what it protects

	STRIX_LEAN is my lean 4-bit tier. The body is ROCmFP4 with the Strix Halo attention K/V
	quality recipe (that is what the STRIX part buys you), and the token embedding table is
	trimmed to Q5_K — that is the LEAN part, the size saving versus my COHERENT tier, which
	keeps the embeddings at Q6_K.

	What never gets trimmed is the head. Every STRIX_LEAN I publish carries the protected Q6_K
	LM head. This model has `tie_word_embeddings: false`, so `output.weight` is a real standalone
	tensor, and a 4-bit head would degrade the logits of every single token. I quantized with
	`--output-tensor-type q6_K` and confirmed the head landed at Q6_K by exact-name read-back
	on the finished file (`output.weight` — exact match, not substring).

	## How I built it

	1. Manifest gate: pulled `ibm-granite/granite-4.2-3b` file list from the HF API with
	`?blobs=true` and recorded the real shard bytes (2 safetensors shards,
	7,319,517,120 bytes total — never the index `total_size`).
	2. Downloaded and byte-verified all 15 files against that manifest (sizes + LFS sha256).
	3. Converted with `convert_hf_to_gguf.py` from my `rocmfpx-dspark-halo` tree (4eca07e),
	`--outtype bf16` → 363 tensors, 7,323,461,696 bytes.
	4. Quantized with the same tree's `llama-quantize` at 16 threads with
	`--output-tensor-type q6_K`. Dry-run estimate 1,967.08 MiB (4.51 bpw); the real file
	landed within ~3.5 MiB of it.

	## Measured on my box — full GPU offload

	amd-halo: AMD Ryzen AI Max+ 395 (Strix Halo, gfx1151), ROCm 7.13.0, 128 GiB unified memory.
	Functional check at full offload — server flags `-dev ROCm0 -fa on -ngl 999 --no-mmap
	-fit off -np 1 -b 2048 -c 8192 -t 16 --jinja`, port 8497, greedy. 8 other llama-server seats
	were live on this machine while I tested (MemAvailable 16.3 GiB before load → 13.2 GiB after),
	so this is a functional check, not an idle-box benchmark.

	\| \| \|
	\|---\|---:\|
	\| offload \| FULL — server log: `offloaded 41/41 layers to GPU`, GTT usage +2.94 GB on load \|
	\| generation (server-reported) \| 60.69 t/s over 128 tokens \|
	\| prompt processing \| 19 tokens in 69.2 ms \|

	Sample output (greedy, prompt "Explain in one clear sentence what granite rock is primarily made of."):

	> Answer: Granite rock is primarily made of quartz. … (continued in the model's native self-check scaffold — real, structured generation)

	## ⚠️ Stock llama.cpp will not load this file

	`Q4_0_ROCMFP4_STRIX_LEAN` is a custom tensor format that exists only in the
	[ROCmFPX](https://github.com/charlie12345/ROCmFPX) fork of llama.cpp.

	```bash
	llama-server -m granite-4.2-3b-Q4_0_ROCMFP4_STRIX_LEAN.gguf -dev ROCm0 -fa on -ngl 999 -c 8192
	```

	## Not measured

	No benchmark sweeps, no context sweeps, no perplexity — one full-offload functional check,
	per my build discipline.

	## Provenance & license

	Converted and quantized from `ibm-granite/granite-4.2-3b` (Apache 2.0). This quantized build
	is released under the same Apache 2.0 license. The ROCmFPX runtime is a third-party fork;
	its own terms apply to the runtime, not to these weights.

	<!-- VARIANTS:START -->

	## All my quants of Granite-4.2-3B

	\| build \| what it is \| size \| tok/s (full GPU offload) \|
	\|---\|---\|---:\|---:\|
	\| [`STRIX_LEAN`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF) \| my leaner 4-bit tier, Q6_K head — smallest of my 4-bit builds, the one most people want \| 1.92 GiB \| 60.69 \|
	\| [`COHERENT`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFP4-COHERENT-GGUF) \| my 4-bit ROCmFP4 tier with the Q6_K-protected head — the balance I run day to day \| 2.04 GiB \| 71.55 \|
	\| [`Q8_0`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFPX-Q8_0-GGUF) \| straight 8-bit ROCmFPX — highest fidelity I publish \| 3.52 GiB \| 46.36 \|
	\| [`Q8_0-AGENT`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFPX-Q8_0-AGENT-GGUF) \| 8-bit ROCmFPX with the agent-tuned tensor set — for tool-calling work where precision matters \| 3.59 GiB \| 50.47 \|

	All measured by me on a Ryzen AI MAX+ 395 (Strix Halo, gfx1151, ROCm 7.2.4) with the whole model on GPU (`-ngl 999`), 128-token greedy generation. A dash means I haven't measured that one yet — I won't put a number in a card I didn't measure.

	Base model: [ibm-granite/granite-4.2-3b](https://huggingface.co/ibm-granite/granite-4.2-3b)
	<!-- VARIANTS:END -->