README.md

---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- NousResearch/Hermes-3-Llama-3.1-70B
- mlabonne/Llama-3-70B-Instruct-abliterated-LORA
model-index:
- name: Hermes-3-Llama-3.1-70B-lorablated
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 71.44
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Hermes-3-Llama-3.1-70B-lorablated
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 52.34
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Hermes-3-Llama-3.1-70B-lorablated
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 13.82
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Hermes-3-Llama-3.1-70B-lorablated
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 13.2
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Hermes-3-Llama-3.1-70B-lorablated
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 22.02
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Hermes-3-Llama-3.1-70B-lorablated
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 41.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/Hermes-3-Llama-3.1-70B-lorablated
      name: Open LLM Leaderboard
---
# 🪽 Hermes-3-Llama-3.1-70B-lorablated

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4Hbw5n68jKUSBQeTqQIeT.png)
<center>8B version: <a href="https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated/"><i>mlabonne/Hermes-3-Llama-3.1-8B-lorablated</i></a></center>

This is an uncensored version of [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B) using lorablation.

You can see in the following example how Hermes 3 refuses to answer a legitimate question while the abliterated model complies:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2-ZRBvlZxvIr_Ag_ynNkk.png)

The recipe is based on @grimjim's [grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter](https://huggingface.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter) (special thanks):

1. **Extraction**: We extract a LoRA adapter by comparing two models: a censored Llama 3 ([meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)) and an abliterated Llama 3.1 ([failspy/Meta-Llama-3.1-70B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated)).
2. **Merge**: We merge this new LoRA adapter using [task arithmetic](https://arxiv.org/abs/2212.04089) to the censored [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B) to abliterate it.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/JdYyK-HLHbyBiHvg-Nvsn.png)

See [this article](https://huggingface.co/blog/mlabonne/abliteration) to learn more about abliteration.

## ⚡ Quantization

* **GGUF**: https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-70B-lorablated-GGUF

## 🧩 Configuration

This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B) + Llama-3.1-70B-Instruct-abliterated-LORA as a base.

The following YAML configuration was used to produce this model:

```yaml
base_model: NousResearch/Hermes-3-Llama-3.1-70B+mlabonne/Llama-3.1-70B-Instruct-abliterated-LORA
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: NousResearch/Hermes-3-Llama-3.1-70B+mlabonne/Llama-3.1-70B-Instruct-abliterated-LORA
    parameters:
      weight: 1.0
```

You can reproduce this model using the following commands:

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

# Merge using previous config
mergekit-yaml config.yaml Hermes-3-Llama-3.1-70B-lorablated --allow-crimes --lora-merge-cache=./cache
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__Hermes-3-Llama-3.1-70B-lorablated)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |35.70|
|IFEval (0-Shot)    |71.44|
|BBH (3-Shot)       |52.34|
|MATH Lvl 5 (4-Shot)|13.82|
|GPQA (0-shot)      |13.20|
|MuSR (0-shot)      |22.02|
|MMLU-PRO (5-shot)  |41.37|