Llama-3.1-70B-Instruct-lorablated-Q4-EXL2

🦙 Llama-3.1-70B-Instruct-lorablated

4 bit EXL2 Quantization

<center>🦙 <a href="https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated"><i>Llama 3.1 8B Instruct abliterated</i></a></center>

This is an uncensored version of Llama 3.1 70B Instruct created with abliteration (see this article to know more about it) using @grimjim's recipe.

More precisely, this is a LoRA-abliterated (lorablated) model:

1. Extraction: We extract a LoRA adapter by comparing two models: a censored Llama 3 and an abliterated Llama 3
2. Merge: We merge this new LoRA adapter using task arithmetic to a censored Llama 3.1 to abliterate it.

I adapted this recipe to Llama 3.1 70B using failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5 and optimized the LoRA rank.

The model is fully uncensored in my tests and maintains a high level of quality. A more rigorous evaluation is still needed to measure the impact of this process on benchmarks.

Special thanks to @grimjim for this technique (see his 8B model) and @FailSpy for his 70B abliterated model. Please follow them if you're interested in abliterated models.

In addition, thanks to brev.dev for providing me with compute!

⚡️ Quantization

• GGUF: https://huggingface.co/mlabonne/Llama-3.1-70B-Instruct-lorablated-GGUF
• Bartowski: https://huggingface.co/bartowski/Llama-3.1-70B-Instruct-lorablated-GGUF (with IQ quants)

🧩 Configuration

This model was merged using the task arithmetic merge method using ./meta-llama/Meta-Llama-3.1-70B-Instruct + Llama-3-70B-Instruct-abliterated-LORA as a base.

The following YAML configuration was used to produce this model:

base_model: meta-llama/Meta-Llama-3.1-70B-Instruct+Llama-3-70B-Instruct-abliterated-LORA
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 80]
    model: meta-llama/Meta-Llama-3.1-70B-Instruct+Llama-3-70B-Instruct-abliterated-LORA
    parameters:
      weight: 1.0

You can reproduce this model using the following commands:

# Setup
git clone https://github.com/arcee-ai/mergekit.git
cd mergekit && pip install -e .
pip install bitsandbytes

# Extraction
mergekit-extract-lora failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5 meta-llama/Meta-Llama-3-70B-Instruct Llama-3-70B-Instruct-abliterated-LORA --rank=64

# Merge using previous config
mergekit-yaml config.yaml Llama-3.1-70B-Instruct-lorablated --allow-crimes --lora-merge-cache=./cache