Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 9
How to use from
llama.cppInstall 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:# Run inference directly in the terminal:
llama cli -hf saishf/Multi-Verse-RP-7B-GGUF: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:# Run inference directly in the terminal:
./llama-cli -hf saishf/Multi-Verse-RP-7B-GGUF: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:# Run inference directly in the terminal:
./build/bin/llama-cli -hf saishf/Multi-Verse-RP-7B-GGUF:Use Docker
docker model run hf.co/saishf/Multi-Verse-RP-7B-GGUF:Quick Links
- 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:
- 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 + jeiku/Re-Host_Limarp_Mistral
- ammarali32/multi_verse_model + jeiku/Luna_LoRA_Mistral
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 |
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf saishf/Multi-Verse-RP-7B-GGUF:# Run inference directly in the terminal: llama cli -hf saishf/Multi-Verse-RP-7B-GGUF: