Instructions to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF 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 QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF: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 QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF: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 QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF 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 QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF 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 QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF to start chatting

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF: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": "QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.B-NIMITA-L3-8B-v0.02-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF: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 QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF: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 "QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF: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"

QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF

This is quantized version of Arkana08/B-NIMITA-L3-8B-v0.02 created using llama.cpp

Original Model Card

(GGUF) Thanks:

mradermacher

GGUF: mradermacher/B-NIMITA-L3-8B-v0.02-GGUF
imatrix GGUF: mradermacher/B-NIMITA-L3-8B-v0.02-i1-GGUF

HumanBoiii

GGUF: HumanBoiii/B-NIMITA-L3-8B-v0.02-Q4_K_M-GGUF

B-NIMITA is an AI model designed to bring role-playing scenarios to life with emotional depth and rich storytelling. At its core is NIHAPPY, providing a solid narrative foundation and contextual consistency. This is enhanced by Mythorica, which adds vivid emotional arcs and expressive dialogue, and V-Blackroot, ensuring character consistency and subtle adaptability. This combination allows B-NIMITA to deliver dynamic, engaging interactions that feel natural and immersive.

Recomended ST Presets:
ChaoticNeutrals - Domain Fusion Presets
Virt-io - SillyTavern-Presets
Arkana08 - SillyTavern-Presets

output-model-directory

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using Arkana08/NIHAPPY-L3.1-8B-v0.09 as a base.

Models Merged

The following models were included in the merge:

Configuration

Primary Model: NIHAPPY (base) - Balancing core narrative flow and contextual awareness. Additional Models:
Mythorica - Enhanced expressive flair, strong emotional arcs, detailed dialogue.
V-Blackroot - Precise focus on character consistency, subtle emotional undertones, adaptability in scene development.

The following YAML configuration was used to produce this model:

models:
  - model: Arkana08/Mythorica-L3-8B
    parameters:
      weight: 0.4
      density: 0.6
  - model: Arkana08/NIHAPPY-L3.1-8B-v0.09
    parameters:
      weight: 0.35
      density: 0.7
  - model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot
    parameters:
      weight: 0.25
      density: 0.55
merge_method: dare_ties
base_model: Arkana08/NIHAPPY-L3.1-8B-v0.09
parameters:
  int8_mask: true
dtype: bfloat16

Credits

Thanks to the creators of the models:

Hastagaras/Jamet-8B-L3-MK.V-Blackroot

Downloads last month: 303

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Papers for QuantFactory/B-NIMITA-L3-8B-v0.02-GGUF

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 36

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19