Instructions to use matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2")
model = AutoModelForCausalLM.from_pretrained("matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2

SGLang

How to use matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/matatonic/Qwen2.5-72B-Instruct-abliterated-v2-6.5bpw-h8-exl2
```

Improve language tag

by lbourdois - opened Apr 27, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+34

-22

Files changed (1) hide show

README.md +34 -22

README.md CHANGED Viewed

@@ -1,23 +1,35 @@
----
-license: other
-license_name: qwen
-license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
-language:
-- en
-pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-72B
-tags:
-- chat
-library_name: transformers
----
-Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction).
-For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy).
-[GGUF](https://huggingface.co/zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF)
-## Try harder to remove admonition and moral appeal
 This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.

+---
+license: other
+license_name: qwen
+license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-72B
+tags:
+- chat
+library_name: transformers
+---
+Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction).
+For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy).
+[GGUF](https://huggingface.co/zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF)
+## Try harder to remove admonition and moral appeal
 This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.