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---
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 | |