Instructions to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M
Use Docker
docker model run hf.co/wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf with Ollama:
ollama run hf.co/wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M
- Unsloth Studio
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf to start chatting
- Pi
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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": "wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 "wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-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 wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf with Docker Model Runner:
docker model run hf.co/wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M
- Lemonade
How to use wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-26B-A4B-it-heretic-zh-RP-gguf-Q4_K_M
List all available models
lemonade list
底座模型:gemma-4-26B-A4B-it-heretic
imatrix基于五个中文类数据集,ctx=2048,chunks=512,剔除所有Emoji以及英文单词行,最终得到596万字纯中文作为输入集。
该模型仅对对于泛化性操作(多语言/编写代码等方面),可能不如其他量化版本。
PPL测试结果:
这里拉了一段完整的RP卡作为评测集,89000字左右,用于评测PPL。
Imatrix:
Q5_K_M: PPL = 41.7158 +/- 1.05441
perplexity: 8.55 seconds per pass - ETA 4.55 minutes
[1]42.7054,[2]30.2909,[3]28.7004,[4]28.0079,[5]27.4540,[6]24.8720,[7]29.1118,[8]28.2985,[9]29.4256,[10]31.2614,[11]31.9318,[12]31.9838,[13]31.3226,[14]30.8078,[15]30.7190,[16]31.2388,[17]30.3229,[18]37.5370,[19]37.4290,[20]38.6303,[21]38.3846,[22]37.5415,[23]37.4147,[24]37.5149,[25]36.6674,[26]36.0477,[27]35.8094,[28]37.5164,[29]37.7974,[30]37.8115,[31]41.7254,[32]41.7158,
Final estimate: PPL = 41.7158 +/- 1.05441
Q4_K_M: PPL = 43.6571 +/- 1.09788
perplexity: 8.32 seconds per pass - ETA 4.43 minutes
[1]40.2634,[2]27.5471,[3]27.2934,[4]25.6302,[5]24.7980,[6]22.6113,[7]27.1034,[8]26.4641,[9]27.4364,[10]29.5109,[11]30.6592,[12]32.4097,[13]31.6274,[14]31.3735,[15]31.3907,[16]31.9458,[17]30.8104,[18]37.2838,[19]36.6702,[20]37.8273,[21]37.3376,[22]36.6424,[23]36.0437,[24]36.5134,[25]36.3847,[26]35.9738,[27]35.9467,[28]37.3036,[29]38.0624,[30]37.9556,[31]41.4221,[32]43.6571,
Final estimate: PPL = 43.6571 +/- 1.09788
IQ4_XS: PPL = 48.6481 +/- 1.27893
perplexity: 6.76 seconds per pass - ETA 3.60 minutes
[1]56.8178,[2]37.9150,[3]35.5188,[4]30.8414,[5]30.0177,[6]26.8122,[7]31.0608,[8]30.8852,[9]32.6146,[10]36.0714,[11]37.6788,[12]37.2891,[13]36.8080,[14]36.4927,[15]37.2115,[16]38.4046,[17]36.9525,[18]45.1330,[19]45.2458,[20]46.7740,[21]46.0036,[22]44.8881,[23]44.7756,[24]45.0148,[25]44.3163,[26]43.0405,[27]43.2473,[28]45.3062,[29]45.9349,[30]45.6817,[31]49.1159,[32]48.6481,
Final estimate: PPL = 48.6481 +/- 1.27893
IQ3_XXS: PPL = 39.3126 +/- 0.93952
perplexity: 6.07 seconds per pass - ETA 3.23 minutes
[1]41.0033,[2]26.1371,[3]24.7727,[4]23.5618,[5]23.3471,[6]23.1080,[7]25.4631,[8]25.6110,[9]27.2752,[10]29.1632,[11]29.0638,[12]29.9699,[13]30.4047,[14]30.0000,[15]30.2590,[16]30.7776,[17]30.2628,[18]36.2746,[19]35.6784,[20]36.9668,[21]36.9711,[22]36.2761,[23]35.7942,[24]35.8439,[25]35.7569,[26]35.1833,[27]35.2329,[28]36.4530,[29]37.0048,[30]37.5546,[31]39.7510,[32]39.3126,
Final estimate: PPL = 39.3126 +/- 0.93952
对比组:
gemma-4-26B-A4B-heretic-APEX-I-Quality: PPL = 48.6938 +/- 1.25948
perplexity: 9.30 seconds per pass - ETA 4.95 minutes
[1]40.7008,[2]30.2781,[3]32.6458,[4]31.6194,[5]30.5565,[6]27.7476,[7]31.7446,[8]31.2653,[9]32.7107,[10]35.0757,[11]36.4731,[12]37.8056,[13]36.4391,[14]35.7723,[15]35.4481,[16]36.4941,[17]35.5500,[18]43.2589,[19]42.9500,[20]45.4924,[21]45.2066,[22]43.5416,[23]43.2288,[24]43.5044,[25]42.8942,[26]41.6601,[27]41.4900,[28]43.5188,[29]44.2139,[30]44.2739,[31]48.6861,[32]48.6938,
Final estimate: PPL = 48.6938 +/- 1.25948
全量Q6_K不使用Imatrix: PPL = 47.5879 +/- 1.23391
perplexity: 9.69 seconds per pass - ETA 5.15 minutes
[1]51.4916,[2]35.0155,[3]32.6720,[4]31.6401,[5]30.4067,[6]28.1260,[7]31.7105,[8]31.1109,[9]32.7684,[10]34.8715,[11]35.9251,[12]37.0230,[13]35.4984,[14]34.8886,[15]34.6195,[16]35.7710,[17]34.8576,[18]43.0041,[19]42.7248,[20]44.0729,[21]43.8856,[22]42.6592,[23]42.1386,[24]42.1939,[25]41.5052,[26]40.2828,[27]40.2352,[28]42.3904,[29]43.0217,[30]43.0792,[31]47.8846,[32]47.5879,
Final estimate: PPL = 47.5879 +/- 1.23391
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Model tree for wwlwwl/gemma-4-26B-A4B-it-heretic-zh-RP-gguf
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google/gemma-4-26B-A4B