Instructions to use kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF to start chatting

How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Gemma-4-E4B-it-ROCmFP4-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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/Gemma-4-E4B-it-ROCmFP4-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"

Gemma-4-E4B-it — ROCmFP4 / ROCmFPX GGUF

First ROCmFP4/ROCmFPX quantization of google/gemma-4-E4B-it. Four quants, built from the Hub BF16 GGUF (no re-conversion from safetensors), each individually verified — text and vision — on real hardware.

Built for AMD Strix Halo (gfx1151) — Ryzen AI MAX+ 395, 128 GB unified memory — using the ROCmFPX llama.cpp fork, which adds AMD-native FP4/FP8 tensor types that mainline llama.cpp does not have.

⚠️ These files need a ROCmFPX-capable llama.cpp build. They will not load in stock llama.cpp / Ollama / LM Studio — Q4_0_ROCMFP4_* and Q*_0_ROCMFPX* are not mainline types.

Variants — all four in this repo

file	ftype	size	BPW	`token_embd`	decode
`gemma-4-E4B-it-Q4_0_ROCMFP4_COHERENT.gguf`	102	4.83 GiB	5.19	q6_K	56.15 t/s
`gemma-4-E4B-it-Q6_0_ROCMFPX_AGENT.gguf`	114	6.96 GiB	7.48	q8_0	39.04 t/s
`gemma-4-E4B-it-Q8_0_ROCMFPX.gguf`	111	7.36 GiB	7.91	q8_0	37.62 t/s
`gemma-4-E4B-it-Q8_0_ROCMFPX_AGENT.gguf`	115	7.43 GiB	7.98	q8_0	37.26 t/s

⚠️ One sample in this table is an outlier (102: 47.21 against a ~56 baseline). The box was otherwise idle and every other sample sits inside a 1.01x band, so this is a transient, not a bimodal model. The median is reported precisely because it is robust to it, and all five raw samples are shown rather than hidden behind a summary.

mmproj-BF16.gguf (vision projector) is included — you need it for image input.

Which to pick: the 4-bit (102). It is the smallest and the fastest by a wide margin, and it answered every correctness and vision test identically to the 8-bits. Take an 8-bit only if you want maximum fidelity for its own sake.

⚠️ Why the 4-bit is 5.76 BPW, not ~4.5

E4B is a MatFormer / per-layer-embedding model: a large fraction of its parameters live in per_layer_token_embd and related tensors that are not driven down to 4 bits. So the 4-bit build is only 1.53× smaller than the 8-bit here, where a conventional dense model would be ~1.8×. This is expected for this architecture, not a defect — the quantized layers really are FP4. One tensor, per_layer_model_proj.weight stays BF16 in all four builds. 720 tensors per file.

Measured — not estimated

Hardware: AMD Ryzen AI MAX+ 395 (Strix Halo, gfx1151), 128 GB unified. Load: -ngl 999 -c 4096 -fa on -fit off. Decode = 300 tokens, warm-up discarded, median of 3.

quant	correctness (3/3)	vision	decode median	runs	spread
102	`17×23=391` · Tokyo · `366`	✅ named all 4 quadrant colours	56.15 t/s	56.55 / 56.15 / 47.21 / 56.15 / 56.02	1.198×
114	`17×23=391` · Tokyo · `366`	✅	39.04 t/s	39.25 / 39.12 / 39.04 / 38.99 / 38.94	1.008×
111	`17×23=391` · Tokyo · `366`	✅	37.62 t/s	37.65 / 37.62 / 37.62 / 37.62 / 37.6	1.001×
115	`17×23=391` · Tokyo · `366`	✅	37.26 t/s	37.26 / 37.26 / 37.25 / 37.22 / 37.26	1.001×

All spreads ≤1.03×, measured with nothing else building on the box — these are clean numbers.

A note on the usual bandwidth sanity check

For a conventional dense model you can sanity-check a decode figure with t/s × file_GB against the platform's peak memory bandwidth (~256 GB/s here) — anything well under ~80% suggests something is misconfigured. That check does not apply to this model. E4B's per-layer-embedding design means not every byte of the file is read per token, so the arithmetic returns well over 100% of "peak" and would happily exceed it. Do not read those percentages as validation for MatFormer/PLE (or MoE) architectures.

Vision was tested with a four-colour quadrant image and the model had to say which colour was where — a solid-colour square would pass trivially and proves nothing. Use -fa off for image input.

⚠️ Speculative decoding (MTP): the draft head loads, but the server aborts — don't use it

Earlier revisions of this card said MTP "does not currently work" and blamed a shared-KV / model-width problem. That explanation was wrong. The draft head initialises correctly (common_speculative_state_draft_mtp: n_embd=2560, backend_sampling=1). What actually happens:

config	result
`-fa on` + `--spec-type draft-mtp`	server aborts in `ggml_cuda_flash_attn_ext_tile_case<512, 512>` (`fattn-tile.cuh:1316`)
`-fa off` + `--spec-type draft-mtp`	loads and drafts (acceptance 0.616) but we could not obtain a stable benchmark

⚠️ This repo does not ship the draft head (an earlier version of this card wrongly said it did). It lives in unsloth/gemma-4-E4B-it-GGUF under MTP/.

The -fa on abort is not a speculation bug at all — it is an uncompiled flash-attention tile case in the HIP backend, and the speculative path is simply the code path that reaches it.

Every decode number above is measured without a drafter, which is the configuration we recommend for this model today.

Verification

Each artifact was checked individually, not sampled:

Size vs --dry-run projection — constant ~15.82 MB GGUF-header delta on all four (spread 5.9 KB) (truncation check)
token_embd audited by exact tensor name
Loaded, 3/3 correctness, vision content test
Decode median of 3 with the spread reported

Exact byte sizes:

5185932160  gemma-4-E4B-it-Q4_0_ROCMFP4_COHERENT.gguf
7472712576  gemma-4-E4B-it-Q6_0_ROCMFPX_AGENT.gguf
7906233216  gemma-4-E4B-it-Q8_0_ROCMFPX.gguf
7974636416  gemma-4-E4B-it-Q8_0_ROCMFPX_AGENT.gguf

⚠️ Note for anyone quantizing this model themselves

gemma-4-E4B-it has tie_word_embeddings = true — there is no output.weight tensor. --output-tensor-type is therefore a silent no-op, and --token-embedding-type is the only flag that actually protects the head:

llama-quantize --output-tensor-type q6_K --token-embedding-type q6_K \
  gemma-4-E4B-it-BF16.gguf out.gguf Q4_0_ROCMFP4_COHERENT 16

Confirm it landed by reading the finished file's token_embd.weight type. And when auditing, do not grep output.weight — it false-matches blk.N.attn_output.weight and will make a healthy build look damaged.