ElaNore-4B
Collection
The newest, most powerful RP model we made, Better than OpenElla and Any other 3B RP Models • 14 items • Updated • 1
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="N-Bot-Int/ElaNore3-4B-GGUFF")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N-Bot-Int/ElaNore3-4B-GGUFF", device_map="auto")How to use N-Bot-Int/ElaNore3-4B-GGUFF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16 # Run inference directly in the terminal: llama cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16 # Run inference directly in the terminal: llama cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
# 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 N-Bot-Int/ElaNore3-4B-GGUFF:F16 # Run inference directly in the terminal: ./llama-cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
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 N-Bot-Int/ElaNore3-4B-GGUFF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
docker model run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16
How to use N-Bot-Int/ElaNore3-4B-GGUFF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N-Bot-Int/ElaNore3-4B-GGUFF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "N-Bot-Int/ElaNore3-4B-GGUFF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16
How to use N-Bot-Int/ElaNore3-4B-GGUFF with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "N-Bot-Int/ElaNore3-4B-GGUFF" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "N-Bot-Int/ElaNore3-4B-GGUFF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "N-Bot-Int/ElaNore3-4B-GGUFF" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "N-Bot-Int/ElaNore3-4B-GGUFF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use N-Bot-Int/ElaNore3-4B-GGUFF with Ollama:
ollama run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "N-Bot-Int/ElaNore3-4B-GGUFF:F16"
}
]
}
}
}# Start Pi in your project directory: pi
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Docker Model Runner:
docker model run hf.co/N-Bot-Int/ElaNore3-4B-GGUFF:F16
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull N-Bot-Int/ElaNore3-4B-GGUFF:F16
lemonade run user.ElaNore3-4B-GGUFF-F16
lemonade list
How to use N-Bot-Int/ElaNore3-4B-GGUFF with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
# 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 N-Bot-Int/ElaNore3-4B-GGUFF:F16
hermes
How to use N-Bot-Int/ElaNore3-4B-GGUFF with OpenClaw:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16
# 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 "N-Bot-Int/ElaNore3-4B-GGUFF:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
# Install Pi:
npm install -g @earendil-works/pi-coding-agent# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "N-Bot-Int/ElaNore3-4B-GGUFF:F16"
}
]
}
}
}# Start Pi in your project directory:
piGGUF with Quants! Allowing you to run models using KoboldCPP and other AI Environments!
| Quant Type | Benefits | Cons |
|---|---|---|
| Q4_K_M | ✅ Smallest size (fastest inference) | ❌ Lowest accuracy compared to other quants |
| ✅ Requires the least VRAM/RAM | ❌ May struggle with complex reasoning | |
| ✅ Ideal for edge devices & low-resource setups | ❌ Can produce slightly degraded text quality | |
| Q5_K_M | ✅ Better accuracy than Q4, while still compact | ❌ Slightly larger model size than Q4 |
| ✅ Good balance between speed and precision | ❌ Needs a bit more VRAM than Q4 | |
| ✅ Works well on mid-range GPUs | ❌ Still not as accurate as higher-bit models | |
| Q8_0 | ✅ Highest accuracy (closest to full model) | ❌ Requires significantly more VRAM/RAM |
| ✅ Best for complex reasoning & detailed outputs | ❌ Slower inference compared to Q4 & Q5 | |
| ✅ Suitable for high-end GPUs & serious workloads | ❌ Larger file size (takes more storage) | |
| (PLEASE VISIT THE mradermacher/ElaNore3-4B-merged-GGUF FOR THE PROPER FULL QUANTS, THIS entry only has THE F16 precission! - Visit Here) |
Read the Model details on huggingface Model Detail Here!
16-bit
Base model
Qwen/Qwen3-4B-Base
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp# Start a local OpenAI-compatible server: llama serve -hf N-Bot-Int/ElaNore3-4B-GGUFF:F16