Instructions to use LetheanNetwork/lemer-bk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

lemer-bk / README.md

lthn

docs: clean header, move methodology below table

1ff6fe8 verified 5 months ago

4.86 kB

language:
  - en
license: eupl-1.2
tags:
  - mlx
  - safetensors
  - 4-bit
  - transformers
  - 8-bit
  - gguf
  - lek
  - lethean
base_model:
  - google/gemma-4-E2B-it
base_model_relation: quantized
pipeline_tag: any-to-any
library_name: mlx
datasets:
  - TIGER-Lab/MMLU-Pro

Lemer

A Gemma 4 E2B finetune by lthn.ai — EUPL-1.2

Benchmarks

Lemer vs Stock Gemma 4 E2B (bf16)

Columns: (Think, Temperature) — G4 = Stock Gemma 4 E2B, Lemer = LEK-activated

	G4(1,0)	G4(1,1)	G4(0,0)	G4(0,1)	Lemer(1,0)	Lemer(1,1)	Lemer(0,0)	Lemer(0,1)
Biology	40.0%	TBC	TBC	TBC	60.0%	TBC	TBC	TBC
Math	10.0%	30.0%	15.0%	10.0%	55.0%	60.0%	25.0%	25.0%
Business	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Chemistry	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Computer Science	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Economics	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Engineering	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Health	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
History	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Law	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Other	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Philosophy	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Physics	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Psychology	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Average	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC

MMLU-Pro (TIGER-Lab/MMLU-Pro, test split, 20 samples per category). Evaluated using rapid-mlx + OpenAI SDK + Google parse_response().

Lemer Quantisation Benchmarks (MMLU-Pro, all categories)

	bf16	8bit	6bit	5bit	4bit	mxfp8	mxfp4	nvfp4
Biology	60.0%	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Math	55.0%	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Business	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Chemistry	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Computer Science	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Economics	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Engineering	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Health	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
History	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Law	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Other	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Philosophy	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Physics	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Psychology	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC
Average	TBC	TBC	TBC	TBC	TBC	TBC	TBC	TBC

Use

MLX (recommended for Apple Silicon)

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("lthn/lemer", revision="4bit")
response = generate(model, tokenizer, prompt="Hello", max_tokens=200)

Rapid-MLX (OpenAI-compatible server)

pip install rapid-mlx
rapid-mlx serve lthn/lemer --port 8100

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8100/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

HF Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("lthn/lemer", revision="bf16-hf")
tokenizer = AutoTokenizer.from_pretrained("lthn/lemer", revision="bf16-hf")

Branches

MLX

Branch	Size
`bf16`	8.7G
`8bit`	4.6G
`6bit`	3.6G
`5bit`	3.0G
`4bit`	2.5G
`mxfp8`	4.5G
`mxfp4`	2.3G
`nvfp4`	2.5G

GGUF

Branch	Size
`bf16-gguf`	8.7G
`8bit-gguf`	4.6G
`6bit-gguf`	3.6G
`5bit-gguf`	3.0G
`4bit-gguf`	2.5G
`3bit-gguf`	2.0G

HF Transformers

Branch	Size
`bf16-hf`	8.7G

Base

google/gemma-4-E2B-it

Licence

Training data and adapter: EUPL-1.2 Base model: Apache 2.0

LetheanNetwork
/

lemer-bk

Lemer

Benchmarks

Lemer vs Stock Gemma 4 E2B (bf16)

Lemer Quantisation Benchmarks (MMLU-Pro, all categories)

Use

MLX (recommended for Apple Silicon)

Rapid-MLX (OpenAI-compatible server)

HF Transformers

Branches

MLX

GGUF

HF Transformers

Base

More

Licence