metadata

license: apache-2.0
language:
  - en
  - zh
base_model: allura-org/Bigger-Body-12b
library_name: transformers
tags:
  - axolotl
  - roleplay
  - conversational
  - chat
  - llama-cpp
  - gguf-my-repo

Triangle104/Bigger-Body-12b-Q4_K_M-GGUF

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

A roleplay-focused pseudo full-finetune of Mistral Nemo Instruct. The successor to the Ink series.

    Testimonials

First impressions (temp 1, min-p .05-.1)

It passes my silly logic tests (read: me trolling random characters) Haven't seen any slop yet Writes short and snappy replies ...yet not too short, like Mahou, and can write longer responses if the context warrants it Follows card formatting instructions

If this holds up to 16K it will be constantly in the hopper alongside Mag-Mell for me. I'm biased towards shorter responses with smarts. :)

Tofumagate

tantalizing writing, leagues better then whatever is available online

Bowza

Fun to use, nice swipe variation, gives me lots to RP off of. Rarely, it'll start to loop, but a quick swipe fixes no problem.

AliCat
```
  Dataset
```

The Bigger Body (referred to as Ink v2.1, because that's still the internal name) mix is absolutely disgusting. It's even more cursed than the original Ink mix.

(Public) Original Datasets

Fizzarolli/limarp-processed Norquinal/OpenCAI - two_users split allura-org/Celeste1.x-data-mixture mapsila/PIPPA-ShareGPT-formatted-named allenai/tulu-3-sft-personas-instruction-following readmehay/medical-01-reasoning-SFT-json LooksJuicy/ruozhiba shibing624/roleplay-zh-sharegpt-gpt4-data CausalLM/Retrieval-SFT-Chat ToastyPigeon/fujin-filtered-instruct

Recommended Settings

Chat template: Mistral v7-tekken (NOT v3-tekken !!!! the main difference is that v7 has specific [SYSTEM_PROMPT] and [/SYSTEM_PROMPT] tags) Recommended samplers (not the be-all-end-all, try some on your own!):

Temp 1.25 / MinP 0.1

    Hyperparams






    


    General

Epochs = 2 LR = 1e-5 LR Scheduler = Cosine Optimizer = Apollo-mini Optimizer target modules = all_linear Effective batch size = 16 Weight Decay = 0.01 Warmup steps = 50 Total steps = 920

    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 for testing and emotional support ilya /platonic

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/Bigger-Body-12b-Q4_K_M-GGUF --hf-file bigger-body-12b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Bigger-Body-12b-Q4_K_M-GGUF --hf-file bigger-body-12b-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/Bigger-Body-12b-Q4_K_M-GGUF --hf-file bigger-body-12b-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Bigger-Body-12b-Q4_K_M-GGUF --hf-file bigger-body-12b-q4_k_m.gguf -c 2048