Instructions to use kingjones777/Gemma-4-E2B-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-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Gemma-4-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Gemma-4-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Gemma-4-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Gemma-4-E2B-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-E2B-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-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Gemma-4-E2B-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-E2B-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-E2B-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-E2B-it-ROCmFP4-GGUF to start chatting

How to use kingjones777/Gemma-4-E2B-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-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Gemma-4-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Gemma-4-E2B-it-ROCmFP4-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Gemma-4-E2B-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-E2B-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-E2B-it-ROCmFP4-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Gemma-4-E2B-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-E2B-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-E2B-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-E2B-it — ROCmFP4 / ROCmFPX GGUF

First ROCmFP4/ROCmFPX quantization of google/gemma-4-E2B-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-E2B-it-Q4_0_ROCMFP4_COHERENT.gguf`	102	3.13 GiB	5.76	q6_K	94.46 t/s
`gemma-4-E2B-it-Q6_0_ROCMFPX_AGENT.gguf`	114	4.38 GiB	8.08	q8_0	71.23 t/s
`gemma-4-E2B-it-Q8_0_ROCMFPX.gguf`	111	4.57 GiB	8.42	q8_0	68.24 t/s
`gemma-4-E2B-it-Q8_0_ROCMFPX_AGENT.gguf`	115	4.60 GiB	8.48	q8_0	68.07 t/s

⚠️ One sample in this table is an outlier (115: 55.05 against a ~68 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.

* The two 8-bit builds were measured while the same machine was quantizing another model. Their spreads (11% and 20%) show that contention, so treat those two as lower bounds — uncontended single-shot observations reached 56.4 and 57.5 t/s. The 4-bit and 6-bit figures were taken clean (spread under 0.4%) and need no such caveat. We would rather publish a conservative number than a flattering one.

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

E2B 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 1.46× 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 [1536, 8960] (~~26 MiB), stays BF16 in all four builds.

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
102	`17×23=391` · Tokyo · `366`	✅ named all 4 quadrant colours	94.46 t/s	94.61 / 94.56 / 94.46 / 94.4 / 94.41
114	`17×23=391` · Tokyo · `366`	✅	71.23 t/s	71.34 / 71.21 / 71.23 / 71.23 / 71.23
111	`17×23=391` · Tokyo · `366`	✅	68.24 t/s	68.27 / 68.28 / 68.15 / 68.24 / 68.23
115	`17×23=391` · Tokyo · `366`	✅	68.07 t/s	68.09 / 67.94 / 68.09 / 68.07 / 55.05

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. E2B's per-layer-embedding design means not every byte of the file is read per token, so the arithmetic returns ~99–102% 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): it works, but it is slower — don't use it

Earlier revisions of this card said MTP "does not currently work". That was wrong, and the reason it was wrong is worth stating: it was tested on one machine whose llama.cpp build never wired the MTP source, and the failure was then explained by a model-width theory that happened to fit. On a build that does wire it (ROCmFPX-2809dc5) the draft head loads and drafts correctly.

It is still not worth enabling here:

config	decode
no drafter	94.42 t/s
`--spec-type draft-mtp`, n-max 3, Q8 drafter	88.54 t/s
draft acceptance	0.589

⚠️ This repo does not ship the draft head (an earlier version of this card wrongly said it did). If you want to reproduce the row above, take MTP/mtp-gemma-4-E2B-it-Q8_0.gguf from unsloth/gemma-4-E2B-it-GGUF. We do not ship it here because it makes this model slower.

0.94x — a net loss at 59% acceptance. Speculation pays inversely to how fast the target already is: on our Gemma-4-31B build the same technique is worth 1.84x at 11.8 t/s, on the 26B-A4B ~1.03-1.15x at ~54 t/s, and here at ~94 t/s it costs more than it returns. The drafter's per-step overhead is roughly fixed; the target's per-token cost is not.

⭐ Acceptance does not predict speedup. A drafter can be right most of the time and still lose.

Verification

Each artifact was checked individually, not sampled:

Size vs --dry-run projection — constant ~15.8 MB GGUF-header delta on all four (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:

3360161632  gemma-4-E2B-it-Q4_0_ROCMFP4_COHERENT.gguf
4707581792  gemma-4-E2B-it-Q6_0_ROCMFPX_AGENT.gguf
4908687200  gemma-4-E2B-it-Q8_0_ROCMFPX.gguf
4941201248  gemma-4-E2B-it-Q8_0_ROCMFPX_AGENT.gguf

⚠️ Note for anyone quantizing this model themselves

gemma-4-E2B-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-E2B-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.