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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("LetheanNetwork/lemer-bk")
model = AutoModelForImageTextToText.from_pretrained("LetheanNetwork/lemer-bk")
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]:]))

llama-cpp-python

How to use LetheanNetwork/lemer-bk with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LetheanNetwork/lemer-bk",
	filename="lemer-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LetheanNetwork/lemer-bk with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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 new

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-server -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

Hermes Agent new

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-server -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

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

Lemer

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

Ollama: ollama run hf.co/lthn/lemer:Q4_K_M

MLX: bf16, 8bit, 6bit, 5bit, 4bit, mxfp8, mxfp4, nvfp4

GGUF: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M

HF Transformers: on main (4-bit NF4 + bf16 in hf-bf16/)

Base

google/gemma-4-E2B-it

Licence

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

Downloads last month: 204

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

Any-to-Any

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for LetheanNetwork/lemer-bk

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(184)

this model

LetheanNetwork
/

lemer-bk

Lemer

Base

More

Licence

Model tree for LetheanNetwork/lemer-bk

Dataset used to train LetheanNetwork/lemer-bk