Triangle104/MN-12b-RP-Ink-Q6_K-GGUF

This model was converted to GGUF format from allura-org/MN-12b-RP-Ink using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

A roleplay-focused LoRA finetune of Mistral Nemo Instruct. Methodology and hyperparams inspired by SorcererLM and Slush. Renamed to Ink to distinguish from [insert every other rp tune ever], but it's the same data as was used in the Teleut RP model. Dataset

The worst mix of data you've ever seen. Like, seriously, you do not want to see the things that went into this model. It's bad.

"this is like washing down an adderall with a bottle of methylated rotgut" - inflatebot Quants

Static GGUFs

Recommended Settings

Chat template: Mistral v3-Tekken Recommended samplers (not the be-all-end-all, try some on your own!):

Temp 1.25 / MinP 0.1
Temp 1.03 / TopK 200 / MinP 0.05 / TopA 0.2

Hyperparams General

Epochs = 2
LR = 6e-5
LR Scheduler = Cosine
Optimizer = Paged AdamW 8bit
Effective batch size = 12

LoRA

Rank = 16
Alpha = 32
Dropout = 0.25 (Inspiration: Slush)

Credits

Humongous thanks to the people who created the data. I would credit you all, but that would be cheating ;) Big thanks to all Allura members, especially Toasty, for testing and emotional support ilya /platonic Also special thanks to Bot for making the model card image here :3 NO thanks to Infermatic. They suck at hosting models

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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/MN-12b-RP-Ink-Q6_K-GGUF --hf-file mn-12b-rp-ink-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/MN-12b-RP-Ink-Q6_K-GGUF --hf-file mn-12b-rp-ink-q6_k.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/MN-12b-RP-Ink-Q6_K-GGUF --hf-file mn-12b-rp-ink-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/MN-12b-RP-Ink-Q6_K-GGUF --hf-file mn-12b-rp-ink-q6_k.gguf -c 2048