README.md

---
library_name: transformers
tags:
- not-for-all-audiences
- mergekit
- llama-cpp
- gguf-my-repo
datasets:
- crestf411/LimaRP-DS
- Gryphe/Sonnet3.5-Charcard-Roleplay
- anthracite-org/c2_logs_32k_mistral-v3_v1.2_no_system
- anthracite-org/kalo-opus-instruct-22k-no-refusal-no-system
- anthracite-org/kalo-opus-instruct-3k-filtered-no-system
- anthracite-org/nopm_claude_writing_fixed
base_model: crestf411/MN-Slush
license: apache-2.0
---

# Triangle104/MN-Slush-Q4_K_M-GGUF
This model was converted to GGUF format from [`crestf411/MN-Slush`](https://huggingface.co/crestf411/MN-Slush) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/crestf411/MN-Slush) for more details on the model.

---
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

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/MN-Slush-Q4_K_M-GGUF --hf-file mn-slush-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/MN-Slush-Q4_K_M-GGUF --hf-file mn-slush-q4_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q4_K_M-GGUF --hf-file mn-slush-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/MN-Slush-Q4_K_M-GGUF --hf-file mn-slush-q4_k_m.gguf -c 2048
```