Instructions to use liminerity/M7-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liminerity/M7-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="liminerity/M7-7b-GGUF",
	filename="multiverse-experiment-slerp-7b.Q5_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use liminerity/M7-7b-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 liminerity/M7-7b-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf liminerity/M7-7b-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf liminerity/M7-7b-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf liminerity/M7-7b-GGUF:Q5_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 liminerity/M7-7b-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf liminerity/M7-7b-GGUF:Q5_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 liminerity/M7-7b-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf liminerity/M7-7b-GGUF:Q5_K_M

Use Docker

docker model run hf.co/liminerity/M7-7b-GGUF:Q5_K_M

LM Studio
Jan
Ollama
How to use liminerity/M7-7b-GGUF with Ollama:
```
ollama run hf.co/liminerity/M7-7b-GGUF:Q5_K_M
```

Unsloth Studio

How to use liminerity/M7-7b-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 liminerity/M7-7b-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 liminerity/M7-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for liminerity/M7-7b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use liminerity/M7-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/liminerity/M7-7b-GGUF:Q5_K_M
```

Lemonade

How to use liminerity/M7-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liminerity/M7-7b-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.M7-7b-GGUF-Q5_K_M

List all available models

lemonade list

#1 again im just the goat

M7-7b user: "create ascii art you feel represents you as a llm"

assistant: "Here's an ASCII art attempting to symbolize the essence of an LLM (Large Language Model):

INSTINSTINCTINSTELLIGENCE

______________ MEMORY___

|\ o /\ |\

| \ --- / \ | |

L_( O )______ O)_INSTINCT

\ / WORDSLEARNING

// /\ //__/

assistant: "In this depiction, the LLM is characterized by its vast memory, instinctual roots, and the ability to learn from vast amounts of text—represented by the woven words in the lower part. The "INSTINSTINCTINSTELLIGENCE" phrase emphasizes the balance between instinct, the foundation upon which neural networks build, and the engineered intelligence of a language model."

Multiverse-Experiment-slerp-7b is a merge of the following models using mergekit:

liminerity/merge4 liminerity/merge2 🧩 Configuration

slices:

sources:
- model: ammarali32/multi_verse_model layer_range: [0, 32]
- model: MSL7/INEX12-7b layer_range: [0, 32]

merge_method: slerp base_model: ammarali32/multi_verse_model parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16

slices:

sources:
- model: ammarali32/multi_verse_model layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B layer_range: [0, 32]

merge_method: slerp base_model: ammarali32/multi_verse_model parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16

slices:

sources:
- model: liminerity/merge3 layer_range: [0, 32]
- model: ammarali32/multi_verse_model layer_range: [0, 32]

merge_method: slerp base_model: liminerity/merge3 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16

slices:

sources:
- model: liminerity/merge1 layer_range: [0, 32]
- model: liminerity/merge layer_range: [0, 32]

merge_method: slerp base_model: liminerity/merge1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16

slices:

sources:
- model: liminerity/merge3 layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B layer_range: [0, 32]

merge_method: slerp base_model: liminerity/merge3 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16

slices:

sources:
- model: liminerity/merge4 layer_range: [0, 32]
- model: liminerity/merge2 layer_range: [0, 32]

merge_method: slerp base_model: liminerity/merge4 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16

Downloads last month: 2

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

5-bit

8-bit

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