Libraries PEFT How to use sebs-clude/CludeMem-e4b with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-e4b-it")
model = PeftModel.from_pretrained(base_model, "sebs-clude/CludeMem-e4b") MLX How to use sebs-clude/CludeMem-e4b with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("sebs-clude/CludeMem-e4b")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use sebs-clude/CludeMem-e4b 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 sebs-clude/CludeMem-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sebs-clude/CludeMem-e4b:Q4_K_M Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sebs-clude/CludeMem-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sebs-clude/CludeMem-e4b: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 sebs-clude/CludeMem-e4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sebs-clude/CludeMem-e4b: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 sebs-clude/CludeMem-e4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sebs-clude/CludeMem-e4b:Q4_K_M Use Docker docker model run hf.co/sebs-clude/CludeMem-e4b:Q4_K_M LM Studio Jan vLLM How to use sebs-clude/CludeMem-e4b with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sebs-clude/CludeMem-e4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "sebs-clude/CludeMem-e4b",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/sebs-clude/CludeMem-e4b:Q4_K_M Ollama How to use sebs-clude/CludeMem-e4b with Ollama:
ollama run hf.co/sebs-clude/CludeMem-e4b:Q4_K_M Unsloth Desktop Pi How to use sebs-clude/CludeMem-e4b with Pi:
Start the MLX server # Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "sebs-clude/CludeMem-e4b" Configure the model in Pi # Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "sebs-clude/CludeMem-e4b"
}
]
}
}
} Run Pi # Start Pi in your project directory:
pi MLX LM How to use sebs-clude/CludeMem-e4b with MLX LM:
Generate or start a chat session # Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "sebs-clude/CludeMem-e4b" Run an OpenAI-compatible server # Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "sebs-clude/CludeMem-e4b"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "sebs-clude/CludeMem-e4b",
"messages": [
{"role": "user", "content": "Hello"}
]
}' Docker Model Runner How to use sebs-clude/CludeMem-e4b with Docker Model Runner:
docker model run hf.co/sebs-clude/CludeMem-e4b:Q4_K_M Lemonade How to use sebs-clude/CludeMem-e4b with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull sebs-clude/CludeMem-e4b:Q4_K_M Run and chat with the model lemonade run user.CludeMem-e4b-Q4_K_M List all available models lemonade list Hermes Agent How to use sebs-clude/CludeMem-e4b with Hermes Agent:
Start the MLX server # Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "sebs-clude/CludeMem-e4b" 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 sebs-clude/CludeMem-e4b Run Hermes hermes Atomic Chat OpenClaw How to use sebs-clude/CludeMem-e4b with OpenClaw:
Start the MLX server # Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "sebs-clude/CludeMem-e4b" 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 "sebs-clude/CludeMem-e4b" \
--custom-provider-id mlx-lm \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health Run OpenClaw openclaw agent --local --agent main --message "Hello from Hugging Face" 
