Triangle104/MN-Slush-Q5_K_M-GGUF

This model was converted to GGUF format from crestf411/MN-Slush 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:

Slush is a two-stage model trained with high LoRA dropout, where stage 1 is a pretraining continuation on the base model, aimed at boosting the model's creativity and writing capabilities. This is then merged into the instruction tune model, and stage 2 is a fine tuning step on top of this to further enhance its roleplaying capabilities and/or to repair any damage caused in the stage 1 merge.

This is still early stage. As always, feedback is welcome, and begone if you demand perfection.

The second stage, like the Sunfall series, follows the Silly Tavern preset (Mistral V2 & V3, though V3-Tekken works fine), so ymmv in particular if you use some other tool and/or preset.

Parameter suggestions:

I did all my testing with temp 1, min-p 0.1, DRY 0.8.

Training details:

Stage 1 (continued pretraining) Target: mistralai/Mistral-Nemo-Base-2407 (resulting LoRA merged into mistralai/Mistral-Nemo-Instruct-2407) LoRA dropout 0.5 (motivation) LoRA rank 64, alpha 128 (motivation) LR cosine 4e-6 LoRA+ with LR Ratio: 15 Context size: 16384 Gradient accumulation steps: 4 Epochs: 1

Stage 2 (fine tune) Target: Stage 1 model LoRA dropout 0.5 LoRA rank 32, alpha 64 LR cosine 5e-6 (min 5e-7) LoRA+ with LR Ratio: 15 Context size: 16384 Gradient accumulation steps: 4 Epochs: 2

Merge Method

This model was merged using the TIES merge method using mistralai/Mistral-Nemo-Base-2407 as a base.

Configuration

The following YAML configuration was used to produce this model:

models:

• model: stage1-on-instruct parameters: weight: 1 density: 1
• model: stage2-on-stage1 parameters: weight: 0.7 density: 1
• model: mistralai/Mistral-Nemo-Instruct-2407 parameters: weight: 1 density: 1 merge_method: ties base_model: mistralai/Mistral-Nemo-Base-2407 parameters: weight: 1 density: 1 normalize: true int8_mask: true tokenizer_source: mistralai/Mistral-Nemo-Instruct-2407 dtype: bfloat16

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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-Slush-Q5_K_M-GGUF --hf-file mn-slush-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/MN-Slush-Q5_K_M-GGUF --hf-file mn-slush-q5_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/MN-Slush-Q5_K_M-GGUF --hf-file mn-slush-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/MN-Slush-Q5_K_M-GGUF --hf-file mn-slush-q5_k_m.gguf -c 2048