---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model: natong19/Qwen2-7B-Instruct-abliterated
inference: false
tags:
- chat
---
# Qwen2-7B-Instruct-abliterated-GGUF
Model: [Qwen2-7B-Instruct-abliterated](https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated)  
Made by: [natong19](https://huggingface.co/natong19)  

Based on original model: [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)  
Created by: [Qwen](https://huggingface.co/Qwen)  

## Quantization notes
Made with llama.cpp-b3154 with imatrix file based on Exllamav2 calibration file.

# Original model card
# 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 |