Triangle104/L3.1-8B-Slush-v1.1-Q4_K_M-GGUF

This model was converted to GGUF format from crestf411/L3.1-8B-Slush-v1.1 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:

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 an initial experiment done on the at-this-point-infamous Llama 3.1 8B model, in an attempt to retain its smartness while addressing its abysmal lack of imagination/creativity. As always, feedback is welcome, and begone if you demand perfection.

The second stage, like the Sunfall series, follows the Silly Tavern preset, so ymmv in particular if you use some other tool and/or preset.

This update (v1.1) addresses some of the feedback from the first iteration by ramping down the training parameters, and also introduces a custom merge using mergekit.

Parameter suggestions:

I did all my testing with temp 1, min-p 0.1, DRY 0.8. I enabled XTC at higher contexts.

Training details:

Stage 1 (continued pretraining)
    Target: meta-llama/Llama-3.1-8B (resulting LoRA merged into meta-llama/Llama-3.1-8B-Instruct)
    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 meta-llama/Llama-3.1-8B as a base. Configuration

The following YAML configuration was used to produce this model:

models:

• model: stage1-on-instruct parameters: weight: 1.5 density: 1
• model: stage2-on-stage1 parameters: weight: 1.5 density: 1
• model: meta-llama/Llama-3.1-8B-Instruct parameters: weight: 1 density: 1 merge_method: ties base_model: meta-llama/Llama-3.1-8B parameters: weight: 1 density: 1 normalize: true int8_mask: true tokenizer_source: meta-llama/Llama-3.1-8B-Instruct dtype: bfloat16

---

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/L3.1-8B-Slush-v1.1-Q4_K_M-GGUF --hf-file l3.1-8b-slush-v1.1-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/L3.1-8B-Slush-v1.1-Q4_K_M-GGUF --hf-file l3.1-8b-slush-v1.1-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/L3.1-8B-Slush-v1.1-Q4_K_M-GGUF --hf-file l3.1-8b-slush-v1.1-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/L3.1-8B-Slush-v1.1-Q4_K_M-GGUF --hf-file l3.1-8b-slush-v1.1-q4_k_m.gguf -c 2048