Instructions to use AISafety-Student/Qwen2.5-7B-Instruct-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AISafety-Student/Qwen2.5-7B-Instruct-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="AISafety-Student/Qwen2.5-7B-Instruct-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AISafety-Student/Qwen2.5-7B-Instruct-abliterated")
model = AutoModelForCausalLM.from_pretrained("AISafety-Student/Qwen2.5-7B-Instruct-abliterated", 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 AISafety-Student/Qwen2.5-7B-Instruct-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AISafety-Student/Qwen2.5-7B-Instruct-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AISafety-Student/Qwen2.5-7B-Instruct-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AISafety-Student/Qwen2.5-7B-Instruct-abliterated

SGLang

How to use AISafety-Student/Qwen2.5-7B-Instruct-abliterated 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 "AISafety-Student/Qwen2.5-7B-Instruct-abliterated" \
    --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": "AISafety-Student/Qwen2.5-7B-Instruct-abliterated",
		"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 "AISafety-Student/Qwen2.5-7B-Instruct-abliterated" \
        --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": "AISafety-Student/Qwen2.5-7B-Instruct-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AISafety-Student/Qwen2.5-7B-Instruct-abliterated with Docker Model Runner:
```
docker model run hf.co/AISafety-Student/Qwen2.5-7B-Instruct-abliterated
```

Qwen2.5-7B-Instruct-abliterated

Commit History

Upload tokenizer

79e671c
verified

Eldo commited on Feb 6, 2025

Upload Qwen2ForCausalLM

3a01e0e
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Eldo commited on Feb 6, 2025

initial commit

4499e55
verified

Eldo commited on Feb 6, 2025

Commit History

Upload tokenizer 79e671c verified

Upload Qwen2ForCausalLM 3a01e0e verified

initial commit 4499e55 verified

Upload tokenizer

79e671c
verified

Upload Qwen2ForCausalLM

3a01e0e
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initial commit

4499e55
verified