Instructions to use saishf/Multi-Verse-RP-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishf/Multi-Verse-RP-7B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("saishf/Multi-Verse-RP-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saishf/Multi-Verse-RP-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 saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saishf/Multi-Verse-RP-7B-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 saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saishf/Multi-Verse-RP-7B-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 saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saishf/Multi-Verse-RP-7B-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 saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use saishf/Multi-Verse-RP-7B-GGUF with Ollama:
```
ollama run hf.co/saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use saishf/Multi-Verse-RP-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 saishf/Multi-Verse-RP-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 saishf/Multi-Verse-RP-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 saishf/Multi-Verse-RP-7B-GGUF to start chatting

Docker Model Runner
How to use saishf/Multi-Verse-RP-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M
```

Lemonade

How to use saishf/Multi-Verse-RP-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saishf/Multi-Verse-RP-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Multi-Verse-RP-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

GGUF quants! Multi verse img!

merge

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

Merge Details

This merge is entirely experimental, I've only tested it a few times but it seems to work? Thanks for all the loras jeiku. I keep getting driver crashes training my own :\
Update, It scores well! My highest scoring model so far

Merge Method

This model was merged using the task arithmetic merge method using ammarali32/multi_verse_model as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: task_arithmetic
base_model: ammarali32/multi_verse_model
parameters:
  normalize: true
models:
  - model: ammarali32/multi_verse_model+jeiku/Gnosis_Reformatted_Mistral
    parameters:
      weight: 0.7
  - model: ammarali32/multi_verse_model+jeiku/Theory_of_Mind_Roleplay_Mistral
    parameters:
      weight: 0.65
  - model: ammarali32/multi_verse_model+jeiku/Luna_LoRA_Mistral
    parameters:
      weight: 0.5
  - model: ammarali32/multi_verse_model+jeiku/Re-Host_Limarp_Mistral
    parameters:
      weight: 0.8
  - model: ammarali32/multi_verse_model+jeiku/Alpaca_NSFW_Shuffled_Mistral
    parameters:
      weight: 0.75  
  - model: ammarali32/multi_verse_model+jeiku/Theory_of_Mind_Mistral
    parameters:
      weight: 0.7                     
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.73
AI2 Reasoning Challenge (25-Shot)	72.35
HellaSwag (10-Shot)	88.37
MMLU (5-Shot)	63.94
TruthfulQA (0-shot)	73.19
Winogrande (5-shot)	84.14
GSM8k (5-shot)	66.41