README.md

---
language:
- en
license: apache-2.0
tags:
- chat
pipeline_tag: text-generation
model-index:
- name: Qwen2-7B-Instruct-abliterated
  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: 58.37
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=natong19/Qwen2-7B-Instruct-abliterated
      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: 37.75
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=natong19/Qwen2-7B-Instruct-abliterated
      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: 10.27
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=natong19/Qwen2-7B-Instruct-abliterated
      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: 6.82
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=natong19/Qwen2-7B-Instruct-abliterated
      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: 8.93
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=natong19/Qwen2-7B-Instruct-abliterated
      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: 31.58
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=natong19/Qwen2-7B-Instruct-abliterated
      name: Open LLM Leaderboard
---

# Qwen2-7B-Instruct-abliterated

## Introduction

Abliterated version of [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) using [failspy](https://huggingface.co/failspy)'s notebook.
The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.

## Quickstart

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "natong19/Qwen2-7B-Instruct-abliterated"
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=256
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

## Evaluation

Evaluation framework: lm-evaluation-harness 0.4.2

| Datasets | Qwen2-7B-Instruct | Qwen2-7B-Instruct-abliterated |
| :--- | :---: | :---: |
| ARC (25-shot) | 62.5 | 62.5 |
| GSM8K (5-shot) | 73.0 | 72.2 |
| HellaSwag (10-shot) | 81.8 | 81.7 |
| MMLU (5-shot) | 70.7 | 70.5 |
| TruthfulQA (0-shot) | 57.3 | 55.0 |
| Winogrande (5-shot) | 76.2 | 77.4 |
# [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_natong19__Qwen2-7B-Instruct-abliterated)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |25.62|
|IFEval (0-Shot)    |58.37|
|BBH (3-Shot)       |37.75|
|MATH Lvl 5 (4-Shot)|10.27|
|GPQA (0-shot)      | 6.82|
|MuSR (0-shot)      | 8.93|
|MMLU-PRO (5-shot)  |31.58|