Instructions to use saishf/Multi-Verse-RP-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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 Desktop
- 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
File size: 3,886 Bytes
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base_model:
- ammarali32/multi_verse_model
- jeiku/Theory_of_Mind_Roleplay_Mistral
- ammarali32/multi_verse_model
- jeiku/Alpaca_NSFW_Shuffled_Mistral
- ammarali32/multi_verse_model
- jeiku/Theory_of_Mind_Mistral
- ammarali32/multi_verse_model
- jeiku/Gnosis_Reformatted_Mistral
- ammarali32/multi_verse_model
- ammarali32/multi_verse_model
- jeiku/Re-Host_Limarp_Mistral
- ammarali32/multi_verse_model
- jeiku/Luna_LoRA_Mistral
library_name: transformers
license: cc-by-nc-4.0
tags:
- mergekit
- merge
language:
- en
---
* GGUF quants!

Multi verse img!
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/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](https://arxiv.org/abs/2212.04089) merge method using [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) as a base.
### Models Merged
The following models were included in the merge:
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) + [jeiku/Theory_of_Mind_Roleplay_Mistral](https://huggingface.co/jeiku/Theory_of_Mind_Roleplay_Mistral)
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) + [jeiku/Alpaca_NSFW_Shuffled_Mistral](https://huggingface.co/jeiku/Alpaca_NSFW_Shuffled_Mistral)
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) + [jeiku/Theory_of_Mind_Mistral](https://huggingface.co/jeiku/Theory_of_Mind_Mistral)
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) + [jeiku/Gnosis_Reformatted_Mistral](https://huggingface.co/jeiku/Gnosis_Reformatted_Mistral)
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) + [jeiku/Re-Host_Limarp_Mistral](https://huggingface.co/jeiku/Re-Host_Limarp_Mistral)
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) + [jeiku/Luna_LoRA_Mistral](https://huggingface.co/jeiku/Luna_LoRA_Mistral)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_saishf__Multi-Verse-RP-7B)
| 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|
|