Instructions to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF", filename="gemma-4-31B-it-speculator.eagle3-F16.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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
Use Docker
docker model run hf.co/williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "williamliao/gemma-4-31B-it-EAGLE3-Speculator-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": "williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
- Ollama
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with Ollama:
ollama run hf.co/williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
- Unsloth Studio
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF to start chatting
- Pi
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
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": "williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
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 "williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M" \ --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 williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with Docker Model Runner:
docker model run hf.co/williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
- Lemonade
How to use williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull williamliao/gemma-4-31B-it-EAGLE3-Speculator-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-31B-it-EAGLE3-Speculator-GGUF-Q4_K_M
List all available models
lemonade list
Gemma 4 31B IT EAGLE3 Speculator GGUF
This repository contains GGUF conversions and quantizations of RedHatAI/gemma-4-31B-it-speculator.eagle3 for use with llama.cpp EAGLE3 speculative decoding.
This is not a standalone chat model. It is an EAGLE3 draft/speculator model and must be used together with the matching target/verifier model.
- Target model:
gemma-4-31B-it - Speculator source:
RedHatAI/gemma-4-31B-it-speculator.eagle3 - Runtime: llama.cpp with
--spec-type draft-eagle3
Files
| File | Type | Notes |
|---|---|---|
gemma-4-31B-it-speculator.eagle3-F16.gguf |
EAGLE3 speculator GGUF | Converted from the original RedHatAI safetensors checkpoint |
gemma-4-31B-it-speculator.eagle3-Q8_0.gguf |
Quantized EAGLE3 speculator GGUF | Quantized from the F16 GGUF |
gemma-4-31B-it-speculator.eagle3-Q4_K_M.gguf |
Quantized EAGLE3 speculator GGUF | Quantized from the F16 GGUF; may be faster for draft decoding |
Usage with llama.cpp
Example:
llama-server \
-m gemma-4-31B-it-Q4_K_M.gguf \
-md gemma-4-31B-it-speculator.eagle3-Q4_K_M.gguf \
--spec-type draft-eagle3 \
--spec-draft-n-max 4 \
--spec-draft-p-min 0.5 \
-c 32768 \
-ngl 99 \
-fa on
Windows CMD example:
llama-server.exe ^
-m gemma-4-31B-it-Q4_K_M.gguf ^
-md gemma-4-31B-it-speculator.eagle3-Q4_K_M.gguf ^
--spec-type draft-eagle3 ^
--spec-draft-n-max 4 ^
--spec-draft-p-min 0.5 ^
-c 32768 ^
-ngl 99 ^
-fa on
PowerShell example:
.\llama-server.exe `
-m "gemma-4-31B-it-Q4_K_M.gguf" `
-md "gemma-4-31B-it-speculator.eagle3-Q4_K_M.gguf" `
--spec-type draft-eagle3 `
--spec-draft-n-max 4 `
--spec-draft-p-min 0.5 `
-c 32768 `
-ngl 99 `
-fa on
Important Notes
This GGUF file is only the draft/speculator model. You still need a compatible GGUF of the target model, such as a GGUF conversion or quantization of gemma-4-31B-it.
Do not use this speculator with unrelated models such as Gemma 4 12B, Gemma 3, Qwen, Llama, Mistral, or other non-matching models. EAGLE3 speculators are target-specific.
Even small differences in the target model, prompt format, quantization, or runtime settings may affect draft acceptance rate and overall speed.
Tested Configuration
Tested with:
Runtime: llama.cpp with EAGLE3 support
Target model: Gemma 4 31B IT GGUF
Draft model: this EAGLE3 GGUF
Example settings:
--spec-type draft-eagle3--spec-draft-n-max 4--spec-draft-p-min 0.5
Local benchmark observations may vary depending on GPU, quantization, context length, batch size, sampling settings, and prompt type.
Benchmark Notes
In local testing, EAGLE3 showed stronger gains on structured or predictable outputs such as:
- code generation
- JSON output
- repeated pattern generation
- summarization
- code completion
It was less effective on some tasks such as translation, creative writing, and some open-ended prompts, where draft acceptance may be lower.
For this speculator, lower-bit draft quantization may perform better than F16 in some llama.cpp setups because the draft model becomes cheaper to run. In local testing, Q4_K_M was a practical candidate for draft decoding, while results may vary across hardware and runtime versions.
On smaller VRAM setups, the extra draft/speculator model may reduce the practical benefit of EAGLE3. In those cases, native MTP models may be more efficient.
Conversion
Converted with llama.cpp convert_hf_to_gguf.py using the original speculator repository and the matching target model directory.
Example conversion command:
python convert_hf_to_gguf.py \
RedHatAI/gemma-4-31B-it-speculator.eagle3 \
--outtype f16 \
--target-model-dir gemma-4-31B-it \
--outfile gemma-4-31B-it-speculator.eagle3-F16.gguf
PowerShell example:
python .\convert_hf_to_gguf.py `
"E:\OLLAMA_MODELS\gemma-4-31B-it-speculator.eagle3" `
--outtype f16 `
--target-model-dir "E:\OLLAMA_MODELS\gemma-4-31B-it" `
--outfile "E:\OLLAMA_MODELS\gemma-4-31B-it-speculator.eagle3-F16.gguf"
Quantization
The F16 GGUF can be quantized with llama-quantize.
Q8_0 example:
llama-quantize \
gemma-4-31B-it-speculator.eagle3-F16.gguf \
gemma-4-31B-it-speculator.eagle3-Q8_0.gguf \
Q8_0
Q4_K_M example:
llama-quantize \
gemma-4-31B-it-speculator.eagle3-F16.gguf \
gemma-4-31B-it-speculator.eagle3-Q4_K_M.gguf \
Q4_K_M
PowerShell example:
.\llama-quantize.exe `
"E:\OLLAMA_MODELS\gemma-4-31B-it-speculator.eagle3-F16.gguf" `
"E:\OLLAMA_MODELS\gemma-4-31B-it-speculator.eagle3-Q4_K_M.gguf" `
Q4_K_M
Credits
Original EAGLE3 speculator model by RedHatAI:
RedHatAI/gemma-4-31B-it-speculator.eagle3
Target model:
gemma-4-31B-it
GGUF support and runtime:
ggml-org/llama.cpp
License
This repository is a converted GGUF version of the original speculator model. The original model license and usage terms apply. Please refer to the upstream repositories for full license details.
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