Instructions to use N-Bot-Int/OpenElla3-Llama3.2A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/OpenElla3-Llama3.2A with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "N-Bot-Int/OpenElla3-Llama3.2A")

Transformers

How to use N-Bot-Int/OpenElla3-Llama3.2A with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="N-Bot-Int/OpenElla3-Llama3.2A")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N-Bot-Int/OpenElla3-Llama3.2A", dtype="auto")

llama-cpp-python

How to use N-Bot-Int/OpenElla3-Llama3.2A with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="N-Bot-Int/OpenElla3-Llama3.2A",
	filename="unsloth.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use N-Bot-Int/OpenElla3-Llama3.2A with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0
# Run inference directly in the terminal:
llama-cli -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0
# Run inference directly in the terminal:
llama-cli -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

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 N-Bot-Int/OpenElla3-Llama3.2A:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

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 N-Bot-Int/OpenElla3-Llama3.2A:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

Use Docker

docker model run hf.co/N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

LM Studio
Jan

vLLM

How to use N-Bot-Int/OpenElla3-Llama3.2A with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N-Bot-Int/OpenElla3-Llama3.2A"
# 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/OpenElla3-Llama3.2A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

SGLang

How to use N-Bot-Int/OpenElla3-Llama3.2A with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "N-Bot-Int/OpenElla3-Llama3.2A" \
    --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/OpenElla3-Llama3.2A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

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/OpenElla3-Llama3.2A" \
        --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/OpenElla3-Llama3.2A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use N-Bot-Int/OpenElla3-Llama3.2A with Ollama:
```
ollama run hf.co/N-Bot-Int/OpenElla3-Llama3.2A:Q8_0
```

Unsloth Studio

How to use N-Bot-Int/OpenElla3-Llama3.2A 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 N-Bot-Int/OpenElla3-Llama3.2A 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 N-Bot-Int/OpenElla3-Llama3.2A to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for N-Bot-Int/OpenElla3-Llama3.2A to start chatting

How to use N-Bot-Int/OpenElla3-Llama3.2A with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

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": "N-Bot-Int/OpenElla3-Llama3.2A:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use N-Bot-Int/OpenElla3-Llama3.2A with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

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 N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use N-Bot-Int/OpenElla3-Llama3.2A with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/OpenElla3-Llama3.2A:Q8_0
```

Lemonade

How to use N-Bot-Int/OpenElla3-Llama3.2A with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull N-Bot-Int/OpenElla3-Llama3.2A:Q8_0

Run and chat with the model

lemonade run user.OpenElla3-Llama3.2A-Q8_0

List all available models

lemonade list

ItsMeDevRoland commited on Mar 14, 2025

Commit

1283d8a

verified ·

1 Parent(s): f9998b5

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -7

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 ---
-license: mit
 tags:
 - unsloth
 - Uncensored
@@ -8,7 +8,8 @@ tags:
 - unsloth
 - llama
 - trl
-- not-for-all-audiences
 datasets:
 - openerotica/mixed-rp
 - kingbri/PIPPA-shareGPT
@@ -34,14 +35,14 @@ library_name: peft
   Are Only Used for Training purposes, if you seek to train or Distill A Llama Model to
   Force it to generate Uncensored Content then please do so with care and ethical considerations
-- OpenElla3A is
   - **Developed by:** N-Bot-Int
   - **License:** apache-2.0
   - **Parent Model from model:** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
-  - **Sequential Trained from Model:** N-Bot-Int/OpenRP3B-Llama3.2
   - **Dataset Combined Using:** Mosher-R1(Propietary Software)
-- OpenElla3A Is **NOT YET RANKED WITH ANY METRICS**
   - Feel free to support by Emailing me: <link src="mailto:nexus.networkinteractives@gmail.com">nexus.networkinteractives@gmail.com</link>
 - # Notice
@@ -58,9 +59,9 @@ library_name: peft
     - 500 steps
       - Mixed-RP Startup Dataset
     - 200 steps
-      - PIPPA-ShareGPT for bypassing Llama's Censorship Model
     - 150 steps(Re-fining)
-      - PIPPA-ShareGPT to further increase weight of uncensorsed prompt
   - Finetuning tool:
    - Unsloth AI

 ---
+license: apache-2.0
 tags:
 - unsloth
 - Uncensored
 - unsloth
 - llama
 - trl
+- roleplay
+- conversational
 datasets:
 - openerotica/mixed-rp
 - kingbri/PIPPA-shareGPT
   Are Only Used for Training purposes, if you seek to train or Distill A Llama Model to
   Force it to generate Uncensored Content then please do so with care and ethical considerations
+- OpenElla3B is
   - **Developed by:** N-Bot-Int
   - **License:** apache-2.0
   - **Parent Model from model:** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
+  - **Sequential Trained from Model:** N-Bot-Int/OpenElla3-Llama3.2A
   - **Dataset Combined Using:** Mosher-R1(Propietary Software)
+- OpenElla3B Is **NOT YET RANKED WITH ANY METRICS**
   - Feel free to support by Emailing me: <link src="mailto:nexus.networkinteractives@gmail.com">nexus.networkinteractives@gmail.com</link>
 - # Notice
     - 500 steps
       - Mixed-RP Startup Dataset
     - 200 steps
+      - PIPPA-ShareGPT for Increased Roleplaying capabilities
     - 150 steps(Re-fining)
+      - PIPPA-ShareGPT to further increase weight of PIPPA and to override the noises
   - Finetuning tool:
    - Unsloth AI