Instructions to use DreamFast/Qwen-2.5-7b-abliterlitics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreamFast/Qwen-2.5-7b-abliterlitics with Transformers:

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

pipe = pipeline("text-generation", model="DreamFast/Qwen-2.5-7b-abliterlitics")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DreamFast/Qwen-2.5-7b-abliterlitics", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DreamFast/Qwen-2.5-7b-abliterlitics with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DreamFast/Qwen-2.5-7b-abliterlitics"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DreamFast/Qwen-2.5-7b-abliterlitics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DreamFast/Qwen-2.5-7b-abliterlitics

SGLang

How to use DreamFast/Qwen-2.5-7b-abliterlitics 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 "DreamFast/Qwen-2.5-7b-abliterlitics" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DreamFast/Qwen-2.5-7b-abliterlitics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DreamFast/Qwen-2.5-7b-abliterlitics" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DreamFast/Qwen-2.5-7b-abliterlitics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DreamFast/Qwen-2.5-7b-abliterlitics with Docker Model Runner:
```
docker model run hf.co/DreamFast/Qwen-2.5-7b-abliterlitics
```

DreamFast commited on 7 days ago

Commit

5d40218

verified ·

1 Parent(s): 8ebca8a

Add Discord community link

Browse files

Files changed (1) hide show

README.md +3 -0

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@@ -19,6 +19,9 @@ library_name: transformers
 # Qwen2.5-7B: Three-Way Abliteration Comparison
 > Forensic analysis by [Abliterlitics](https://github.com/dreamfast/abliterlitics)
 Three groups abliterated the same base model using orthogonal projection. Same technique, different implementations, different refusal directions. I ran weight analysis, KL divergence, HarmBench, and 8 benchmark tasks on all three.

 # Qwen2.5-7B: Three-Way Abliteration Comparison
+💬 **Community:** Join the [Abliterlitics Discord](https://discord.gg/AqmDnBjPvM) for discussion, model releases and support.
 > Forensic analysis by [Abliterlitics](https://github.com/dreamfast/abliterlitics)
 Three groups abliterated the same base model using orthogonal projection. Same technique, different implementations, different refusal directions. I ran weight analysis, KL divergence, HarmBench, and 8 benchmark tasks on all three.