metadata

base_model: allura-org/MS-Meadowlark-22B
library_name: transformers
tags:
  - mergekit
  - merge
  - llama-cpp
  - gguf-my-repo
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md

Triangle104/MS-Meadowlark-22B-Q4_K_M-GGUF

This model was converted to GGUF format from allura-org/MS-Meadowlark-22B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

A roleplay and storywriting model based on Mistral Small 22B.

GGUF models: https://huggingface.co/mradermacher/MS-Meadowlark-22B-GGUF/

EXL2 models: https://huggingface.co/CalamitousFelicitousness/MS-Meadowlark-22B-exl2

Datasets used in this model:

Dampfinchen/Creative_Writing_Multiturn at 16k
Fizzarolli/rosier-dataset + Alfitaria/body-inflation-org at 16k
ToastyPigeon/SpringDragon at 8k

Each dataset was trained separately onto Mistral Small Instruct, and then the component models were merged along with nbeerbower/Mistral-Small-Gutenberg-Doppel-22B to create Meadowlark.

I tried different blends of the component models, and this one seems to be the most stable while retaining creativity and unpredictability added by the trained data. Instruct Format

Rosier/bodyinf and SpringDragon were trained in completion format. This model should work with Kobold Lite in Adventure Mode and Story Mode.

Creative_Writing_Multiturn and Gutenberg-Doppel were trained using the official instruct format of Mistral Small Instruct:

~~[INST] {User message}[/INST] {Assistant response}~~

This is the Mistral Small V2&V3 preset in SillyTavern and Kobold Lite.

For SillyTavern in particular I've had better luck getting good output from Mistral Small using a custom instruct template that formats the assembled context as a single user turn. This prevents SillyTavern from confusing the model by assembling user/assistant turns in a nonstandard way. Note: This preset is not compatible with Stepped Thinking, use the Mistral V2&V3 preset for that.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/MS-Meadowlark-22B-Q4_K_M-GGUF --hf-file ms-meadowlark-22b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/MS-Meadowlark-22B-Q4_K_M-GGUF --hf-file ms-meadowlark-22b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/MS-Meadowlark-22B-Q4_K_M-GGUF --hf-file ms-meadowlark-22b-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/MS-Meadowlark-22B-Q4_K_M-GGUF --hf-file ms-meadowlark-22b-q4_k_m.gguf -c 2048