Any-to-Any
Transformers
Safetensors
GGUF
English
gemma4
image-text-to-text
4-bit precision
8-bit precision
bitsandbytes
conversational
Instructions to use LetheanNetwork/lemer-bk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LetheanNetwork/lemer-bk with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("LetheanNetwork/lemer-bk") model = AutoModelForMultimodalLM.from_pretrained("LetheanNetwork/lemer-bk", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LetheanNetwork/lemer-bk 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 LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: llama cli -hf LetheanNetwork/lemer-bk:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: llama cli -hf LetheanNetwork/lemer-bk: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 LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LetheanNetwork/lemer-bk: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 LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LetheanNetwork/lemer-bk:Q4_K_M
Use Docker
docker model run hf.co/LetheanNetwork/lemer-bk:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LetheanNetwork/lemer-bk with Ollama:
ollama run hf.co/LetheanNetwork/lemer-bk:Q4_K_M
- Unsloth Studio
How to use LetheanNetwork/lemer-bk 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 LetheanNetwork/lemer-bk 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 LetheanNetwork/lemer-bk to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LetheanNetwork/lemer-bk to start chatting
- Pi
How to use LetheanNetwork/lemer-bk with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LetheanNetwork/lemer-bk: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": "LetheanNetwork/lemer-bk:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use LetheanNetwork/lemer-bk with Docker Model Runner:
docker model run hf.co/LetheanNetwork/lemer-bk:Q4_K_M
- Lemonade
How to use LetheanNetwork/lemer-bk with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LetheanNetwork/lemer-bk:Q4_K_M
Run and chat with the model
lemonade run user.lemer-bk-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use LetheanNetwork/lemer-bk with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LetheanNetwork/lemer-bk: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 LetheanNetwork/lemer-bk:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use LetheanNetwork/lemer-bk with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LetheanNetwork/lemer-bk: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 "LetheanNetwork/lemer-bk: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"
docs: remove preliminary benchmarks pending official eval
Browse files
README.md
CHANGED
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@@ -31,52 +31,6 @@ A [Gemma 4 E2B](https://huggingface.co/google/gemma-4-E2B-it) finetune by [lthn.
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**HF Transformers**: on main (4-bit NF4 + bf16 in hf-bf16/)
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## Benchmarks
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### Eval: MMLU Pro
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Columns: **(Think, Temperature)** — `G4` = Stock Gemma 4 E2B, `Lemer` = LEK-activated
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| | G4(1,0) | G4(1,1) | G4(0,0) | G4(0,1) | Lemer(1,0) | Lemer(1,1) | Lemer(0,0) | Lemer(0,1) |
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| :---- | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |
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| Biology | 40.0% | TBC | TBC | TBC | **60.0%** | [**65.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "65.0") | TBC | TBC |
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| Math | 10.0% | 30.0% | 15.0% | 10.0% | **55.0%** | [**80.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "80.0") | **25.0%** | **25.0%** |
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| Business | TBC | TBC | TBC | 40.0% | TBC | [**50.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "50.0") | TBC | TBC |
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| Chemistry | TBC | TBC | TBC | 15.0% | TBC | [**35.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "35.0") | TBC | TBC |
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| Computer Science | TBC | TBC | TBC | 65.0% | TBC | [**65.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "65.0") | TBC | TBC |
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| Economics | TBC | TBC | TBC | 25.0% | TBC | [**45.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "45.0") | TBC | TBC |
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| Engineering | TBC | TBC | TBC | 50.0% | TBC | [**55.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "55.0") | TBC | TBC |
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| Health | TBC | TBC | TBC | 25.0% | TBC | [**35.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "35.0") | TBC | TBC |
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| History | TBC | TBC | TBC | 5.0% | TBC | [**15.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "15.0") | TBC | TBC |
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| Law | TBC | TBC | TBC | 15.0% | TBC | [**25.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "25.0") | TBC | TBC |
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| Other | TBC | TBC | TBC | 45.0% | TBC | [**55.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "55.0") | TBC | TBC |
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| Philosophy | TBC | TBC | TBC | 25.0% | TBC | [**30.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "30.0") | TBC | TBC |
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| Physics | TBC | TBC | TBC | 45.0% | TBC | [**50.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "50.0") | TBC | TBC |
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| Psychology | TBC | TBC | TBC | 20.0% | TBC | [**45.0%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "45.0") | TBC | TBC |
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| **Average** | TBC | TBC | TBC | **33.6%** | TBC | [**46.4%**](https://huggingface.co/lthn/lemer/blob/bf16/README.md#mmlu-pro-bf16-best-per-category "46.4") | TBC | TBC |
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### Lemer Quantisation Benchmarks (MMLU-Pro, all categories)
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| | [bf16](https://huggingface.co/lthn/lemer/tree/bf16) | [8bit](https://huggingface.co/lthn/lemer/tree/8bit) | [6bit](https://huggingface.co/lthn/lemer/tree/6bit) | [5bit](https://huggingface.co/lthn/lemer/tree/5bit) | [4bit](https://huggingface.co/lthn/lemer/tree/4bit) | [mxfp8](https://huggingface.co/lthn/lemer/tree/mxfp8) | [mxfp4](https://huggingface.co/lthn/lemer/tree/mxfp4) | [nvfp4](https://huggingface.co/lthn/lemer/tree/nvfp4) |
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| Biology | 65.0% | TBC | TBC | TBC | 45.0% | TBC | TBC | TBC |
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| Math | 60.0% | TBC | TBC | TBC | 50.0% | TBC | TBC | TBC |
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| Business | 45.0% | TBC | TBC | TBC | 40.0% | TBC | TBC | TBC |
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| Chemistry | 20.0% | TBC | TBC | TBC | 15.0% | TBC | TBC | TBC |
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| Computer Science | 50.0% | TBC | TBC | TBC | 65.0% | TBC | TBC | TBC |
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| Economics | 25.0% | TBC | TBC | TBC | 25.0% | TBC | TBC | TBC |
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| Engineering | 50.0% | TBC | TBC | TBC | 50.0% | TBC | TBC | TBC |
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| Health | 25.0% | TBC | TBC | TBC | 25.0% | TBC | TBC | TBC |
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| History | 10.0% | TBC | TBC | TBC | 5.0% | TBC | TBC | TBC |
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| Law | 25.0% | TBC | TBC | TBC | 15.0% | TBC | TBC | TBC |
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| Other | 45.0% | TBC | TBC | TBC | 45.0% | TBC | TBC | TBC |
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| Philosophy | 20.0% | TBC | TBC | TBC | 25.0% | TBC | TBC | TBC |
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| Physics | 50.0% | TBC | TBC | TBC | 45.0% | TBC | TBC | TBC |
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| Psychology | 25.0% | TBC | TBC | TBC | 20.0% | TBC | TBC | TBC |
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| **Average** | **36.8%** | TBC | TBC | TBC | **33.6%** | TBC | TBC | TBC |
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## Base
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[google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it)
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**HF Transformers**: on main (4-bit NF4 + bf16 in hf-bf16/)
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## Base
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[google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it)
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