Text Generation
PEFT
Safetensors
GGUF
Russian
English
gemma-4
lora
russian
english
gen-z
slang
cringe
post-irony
conversational
Instructions to use 3ndetz/zoomerlm-gemma4-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 3ndetz/zoomerlm-gemma4-e2b with PEFT:
Task type is invalid.
- llama-cpp-python
How to use 3ndetz/zoomerlm-gemma4-e2b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="3ndetz/zoomerlm-gemma4-e2b", filename="mmproj-zoomerlm-gemma4-e2b-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 3ndetz/zoomerlm-gemma4-e2b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf 3ndetz/zoomerlm-gemma4-e2b: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 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf 3ndetz/zoomerlm-gemma4-e2b: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 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
Use Docker
docker model run hf.co/3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use 3ndetz/zoomerlm-gemma4-e2b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "3ndetz/zoomerlm-gemma4-e2b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "3ndetz/zoomerlm-gemma4-e2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
- Ollama
How to use 3ndetz/zoomerlm-gemma4-e2b with Ollama:
ollama run hf.co/3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
- Unsloth Studio
How to use 3ndetz/zoomerlm-gemma4-e2b 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 3ndetz/zoomerlm-gemma4-e2b 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 3ndetz/zoomerlm-gemma4-e2b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for 3ndetz/zoomerlm-gemma4-e2b to start chatting
- Pi
How to use 3ndetz/zoomerlm-gemma4-e2b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf 3ndetz/zoomerlm-gemma4-e2b: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": "3ndetz/zoomerlm-gemma4-e2b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use 3ndetz/zoomerlm-gemma4-e2b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf 3ndetz/zoomerlm-gemma4-e2b: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 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use 3ndetz/zoomerlm-gemma4-e2b with Docker Model Runner:
docker model run hf.co/3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
- Lemonade
How to use 3ndetz/zoomerlm-gemma4-e2b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull 3ndetz/zoomerlm-gemma4-e2b:Q4_K_M
Run and chat with the model
lemonade run user.zoomerlm-gemma4-e2b-Q4_K_M
List all available models
lemonade list
| [ | |
| { | |
| "model": "base", | |
| "tag": "describe", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "Describe what you see in this image. Be specific about objects, colors, layout.", | |
| "content": "This image appears to be a screenshot of a software interface, likely related to machine learning, deep learning, or some kind of data visualization/training process. The layout is divided into several distinct sections.\n\nHere is a detailed breakdown of what I see:\n\n**Overall Layout:**\nThe image is structured with a main central area and side panels, suggesting a typical application interface. There are several text labels in Cyrillic (Russian) and English, indicating a multilingual or localized interface.\n\n**Left Side Content (Text and Input/Output):**\n* **Top Left:** The word \"MIRROR\" is visible, followed by \"realtime persona LoRA\".\n* **Middle Left:** There are several lines of text in Russian, such as:\n * \"Отлично, привет! Я просто занят, но на связи\"\n * \"Давай еще короче, короче пацаны, братан слышь и короч в общем\"\n* **Bottom Left:** More text and buttons are present:\n * \"MIRROR\"\n * \"Давай еще короче, короче пацаны, братан слышь и короч в общем\"\n * Below this, there are buttons labeled \"HEARS\", \"SAY\", and \"Слышь\".\n * At the very bottom, there is a status message: \"loading model (0.0s)\".\n\n**Center/Right Side Content (Model/Training Area):**\nThis area is dominated by graphical representations and further text prompts.\n\n* **Upper Center/Right:**\n * A section labeled \"YOU\" with the text \"привет, как дела?\"\n* **Middle Right:**\n * A section labeled \"MODEL\" with the text \"LoRA-targeted slabs glow during training\".\n * Below this, there is a button labeled \"TRAINING", | |
| "reasoning_content": "", | |
| "finish": "length", | |
| "time_s": 2.922000000020489 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "count_panels", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "How many distinct panels or game tiles do you see? Just the number.", | |
| "content": "10", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.2970000000204891 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "zoomer_describe_ru", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "Опиши что на картинке как зумер. 1-2 предложения, неформально.", | |
| "content": "На картинке какой-то закрученный процесс, типа кто-то там что-то настраивает, выглядит как какой-то арт-проект или обучение чему-то сложному. 🤪", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.5619999999762513 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "identify_text", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "Quote 2 short pieces of text visible in the image, in their original language.", | |
| "content": "Here are two short pieces of text visible in the image, in their original language:\n\n1. **МИРОР // realtime persona LoRA**\n2. **Давай еще короче, короче пацаны, братан слышь и короч в общем**", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.6880000000237487 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "describe", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "Describe what you see in this image. Be specific about objects, colors, layout.", | |
| "content": "This appears to be a screenshot from a machine learning or reinforcement learning environment, likely showing the progress of a training process.\n\nHere is a detailed breakdown of what I see:\n\n**Overall Layout:**\nThe image is divided into two main sections:\n1. **Left Side:** A grid of visual representations, likely showing game states or environments.\n2. **Right Side:** A text-based interface displaying metrics, statistics, and controls related to the training.\n\n**Left Side (Visual Grid):**\nThis section consists of a 3x4 grid of smaller images.\n* **Content:** Each image appears to be a top-down view of a game board or environment, possibly a variation of a strategy game or a simulation. The boards are dark, featuring a grid pattern and various small, colored objects or pieces scattered across them.\n* **Color Palette:** The dominant colors in the boards are dark grays and blacks, with accents of green, red, and possibly blue for the pieces.\n* **Labeling:** Above the grid, there is a title: \"**PRETRAINING – live batches**\". Below the grid, there is a status bar showing statistics for these batches: \"policies fight each other in 16 parallel worlds – watch the WATCH phase\".\n\n**Right Side (Interface/Metrics):**\nThis section is dominated by text and numerical data, organized into several blocks.\n\n* **Top Section (Live Training):**\n * Title: \"**LIVE TRAINING**\"\n * Metrics displayed include: `loss`, `teamm reward`, `teami reward`, `kills/env`.\n * There are controls/buttons: `[S] save`, `[L] load`, `[R] reset`.\n\n* **Middle Section (Training Parameters):**\n * Title: \"**KNOBS**\" (This might refer to configuration settings).", | |
| "reasoning_content": "", | |
| "finish": "length", | |
| "time_s": 2.639999999955762 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "count_panels", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "How many distinct panels or game tiles do you see? Just the number.", | |
| "content": "12", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.28200000000651926 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "zoomer_describe_ru", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "Опиши что на картинке как зумер. 1-2 предложения, неформально.", | |
| "content": "Это, типа, какая-то игра про обучение, где эти ребята пытаются научиться чему-то, пока не сойдут с ума. 🤪", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.46799999999348074 | |
| }, | |
| { | |
| "model": "base", | |
| "tag": "identify_text", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "Quote 2 short pieces of text visible in the image, in their original language.", | |
| "content": "Here are two short pieces of text visible in the image, in their original language:\n\n1. **PRETRAINING – live batches**\n2. **LIVE TRAINING**", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.5 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "describe", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "Describe what you see in this image. Be specific about objects, colors, layout.", | |
| "content": "this is a screenshot from a game or app, possibly a roleplaying or social game, showing a chat interface on the left and a visual representation on the right.\n\n**left side (chat/text):**\n* the user \"mirror\" is typing something.\n* mirror says \"отлично, привет! я просто занят, но на связи\" (great, hi! i'm just busy but online).\n* mirror then says \"давай еще короче, короче пацаны, бранч слёж и короче вобщем\" (let's go faster, guys, let's go and just be done).\n\n**right side (visual/model):**\n* this is a vertical, segmented visual representation, possibly a character or a timeline.\n* it's divided into several horizontal colored blocks (teal, light blue, purple, etc.).\n* the top section is mostly a dark purple/black background.\n* the bottom section has a lighter, more pastel-like color scheme.\n* there's a small green line graph at the very bottom showing a decline in \"lora loss\" over time.\n* the text \"training\" is visible near the bottom right.\n\n**overall vibe:** it looks like a chaotic, low-effort interaction in a game, with a visual element that seems to be reacting to the text chat.", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 2.5469999999622814 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "count_panels", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "How many distinct panels or game tiles do you see? Just the number.", | |
| "content": "12", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.3440000000409782 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "zoomer_describe_ru", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "Опиши что на картинке как зумер. 1-2 предложения, неформально.", | |
| "content": "чё за кринж тут, пацаны, это же чистый скролл-шоу. 💀", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.46899999998277053 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "identify_text", | |
| "image": "mirror_v2_training.png", | |
| "prompt": "Quote 2 short pieces of text visible in the image, in their original language.", | |
| "content": "Here are two short pieces of text from the image:\n\n1. **\"привет, как дела?\"** (from the \"you\" side)\n2. **\"давай ец короче, короче пацаны, братан слышь и короче в общем\"** (from the \"mirror\" side)", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.7179999999934807 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "describe", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "Describe what you see in this image. Be specific about objects, colors, layout.", | |
| "content": "this is a screenshot from a game called \"betrayal\" or something similar, it looks like a multiplayer game where you control avatars on a grid.\n\nthe layout is a 3x3 grid of gameplay areas, each showing a similar-looking grid with some small figures (avatars) on it. the background is a dark grey or black.\n\nthe left side is a series of 3x3 grids, each representing a different player's view or a specific area. the avatars are small, colored figures.\n\nthe right side is a text-based interface with stats for each player (e.g., \"tacke: 12\", \"betrayal: 1\", \"kills/env: 1\"). there's also a section for \"live training\" with sliders for \"training intensity\" and \"learning rate\".\n\nthe bottom row shows the player's own view, which is a grid with some avatars on it.\n\nthe overall aesthetic is very minimalist and pixelated, like an old-school rpg or strategy game. the colors are mostly muted greys and greens for the terrain, with the avatars having various colors.", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 1.7660000000032596 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "count_panels", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "How many distinct panels or game tiles do you see? Just the number.", | |
| "content": "9", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.3119999999762513 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "zoomer_describe_ru", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "Опиши что на картинке как зумер. 1-2 предложения, неформально.", | |
| "content": "ну это же какой-то вайб от ботс, типа мы все в одном месте сидим и тусим. типа фейк-вайб, но по факту просто сидим и чиллим.", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.5790000000270084 | |
| }, | |
| { | |
| "model": "trained", | |
| "tag": "identify_text", | |
| "image": "arena_shooter_v3_pretrain.png", | |
| "prompt": "Quote 2 short pieces of text visible in the image, in their original language.", | |
| "content": "Here are two short pieces of text from the image:\n\n1. **\"live baches\"** (top left)\n2. **\"live training\"** (right side)", | |
| "reasoning_content": "", | |
| "finish": "stop", | |
| "time_s": 0.5459999999729916 | |
| } | |
| ] |