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Zabuza-8B-Llama-3.1-Q6_K-GGUF

Text Generation
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llama-cpp
gguf-my-repo
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metadata
base_model: Hastagaras/Zabuza-8B-Llama-3.1
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
tags:
  - mergekit
  - merge
  - not-for-all-audiences
  - llama-cpp
  - gguf-my-repo

Triangle104/Zabuza-8B-Llama-3.1-Q6_K-GGUF

This model was converted to GGUF format from Hastagaras/Zabuza-8B-Llama-3.1 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:

This model is a combination of merge, abliteration technique (using baukit) and finetuning.

The base model is arcee-ai/Llama-3.1-SuperNova-Lite, which underwent abliteration to reduce model refusals.

Next, I finetuned the abliterated SuperNova-Lite with 10K diverse examples such as:

Claude and Gemini Instruction/RP (15k sloppy examples were removed!, but some may have slipped through.)
Human-written Stories/RP (Most stories have dialogue)
IFEval-like data (To preserve the model's instruction following ability)
Harmful data (To remove disclaimers and moralizing responses, but not 100% disappear.)
My sarcastic and rude AI assistant data (Just for my personal satisfaction)

Lastly, I merged the model using TIES, inspired by this MERGE by Joseph717171.

Chat Template

Llama 3.1 Instruct

<|start_header_id|>{role}<|end_header_id|>

{message}<|eot_id|><|start_header_id|>{role}<|end_header_id|>

{message}<|eot_id|>

System messages for role-playing should be very detailed if you don't want dry responses.

Configuration

This is a merge of pre-trained language models created using mergekit.

The following YAML configuration was used to produce this model:

models:

  • model: Hastagaras/snovalite-baukit-6-14.FT-L5-7.13-22.27-31 parameters: weight: 1 density: 1

  • model: Hastagaras/snovalite-baukit-6-14.FT-L5-7.13-22.27-31 parameters: weight: 1 density: 1

merge_method: ties base_model: meta-llama/Llama-3.1-8B parameters: density: 1 normalize: true int8_mask: true 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/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.gguf -p "The meaning to life and the universe is"

Server: 

llama-server --hf-repo Triangle104/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-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/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.gguf -p "The meaning to life and the universe is"

or 

./llama-server --hf-repo Triangle104/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.gguf -c 2048