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

Libraries

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF",
	filename="L3-Aspire-Heart-Matrix-8B.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF 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 "QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF" \
    --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": "QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF",
		"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 "QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF" \
        --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": "QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-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/L3-Aspire-Heart-Matrix-8B-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/L3-Aspire-Heart-Matrix-8B-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/L3-Aspire-Heart-Matrix-8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3-Aspire-Heart-Matrix-8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF

This is quantized version of ZeroXClem/L3-Aspire-Heart-Matrix-8B created using llama.cpp

Original Model Card

ZeroXClem/L3-Aspire-Heart-Matrix-8B

ZeroXClem/L3-Aspire-Heart-Matrix-8B is an experimental language model crafted by merging three high-quality 8B parameter models using the Model Stock Merge method. This synthesis leverages the unique strengths of Aspire, Heart Stolen, and CursedMatrix, creating a highly versatile and robust language model for a wide array of tasks.

🌟 Model Details

Name: ZeroXClem/L3-Aspire-Heart-Matrix-8B
Base Model: Khetterman/CursedMatrix-8B-v9
Merge Method: Model Stock
Parameter Count: 8 billion
Precision: bfloat16

📋 Models Used in the Merge

Aspire
Creator: DreadPoor
Known for exceptional performance across diverse tasks and benchmarks.
Heart Stolen
Creator: DreadPoor
Renowned for its creative and empathetic prowess.
CursedMatrix
Creator: Khetterman
Famous for its depth and complexity, particularly in creative writing and roleplay.

⚙️ Merge Configuration

models:
  - model: DreadPoor/Aspire-8B-model_stock
  - model: DreadPoor/Heart_Stolen-8B-Model_Stock
  - model: Khetterman/CursedMatrix-8B-v9
merge_method: model_stock
base_model: Khetterman/CursedMatrix-8B-v9
normalize: false
int8_mask: true
dtype: bfloat16

🌌 Model Capabilities

This powerful merger unites the best features of its components:

Aspire: Outstanding performance across general tasks and benchmarks.
Heart Stolen: Creativity and empathy at its core.
CursedMatrix: Mastery of complex and dynamic text generation.

The resulting model excels in:

🌟 General Question Answering
📝 Creative Writing
✂️ Summarizing Long-Form Content
🎭 Roleplay Scenarios
✅ Task Completion and Problem-Solving

🛠️ Usage

This model is compatible with popular inference frameworks, including:

vLLM
LMStudio
Hugging Face Transformers and other major libraries.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ZeroXClem/L3-Aspire-Heart-Matrix-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What are the fundamentals of python programming?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=100)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Whether you're fine-tuning for specific tasks or using it out of the box, this model is a good base for your applications.

Please give us any feedback if issues arise during inference via the discussions tab.

⚖️ Ethical Considerations

Given its uncensored origins and the potential for emergent behaviors, users should exercise caution. Be mindful of:

Potential biases in outputs.
Unexpected or unpredictable behavior in uncensored settings.

Best Practices: Implement robust content filtering and ensure responsible deployment in production environments.

🙏 Acknowledgements

A heartfelt thank-you to the creators of the original models:

DreadPoor for Aspire and Heart Stolen.
Khetterman for CursedMatrix.

Your brilliant contributions made this merge a reality.

📜 License

This model inherits the licensing terms of its base components. Please refer to the licenses of:

Ensure compliance with all licensing requirements when using this model.

Downloads last month: 82

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/L3-Aspire-Heart-Matrix-8B-GGUF

DreadPoor/Aspire-8B-model_stock

DreadPoor/Heart_Stolen-8B-Model_Stock

Khetterman/CursedMatrix-8B-v9

Merge model

this model