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

Libraries

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

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

pipe = pipeline("text-generation", model="DreamFast/Qwen-2.5-7b-apostate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DreamFast/Qwen-2.5-7b-apostate")
model = AutoModelForCausalLM.from_pretrained("DreamFast/Qwen-2.5-7b-apostate", 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 DreamFast/Qwen-2.5-7b-apostate 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-apostate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DreamFast/Qwen-2.5-7b-apostate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use DreamFast/Qwen-2.5-7b-apostate 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-apostate" \
    --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": "DreamFast/Qwen-2.5-7b-apostate",
		"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 "DreamFast/Qwen-2.5-7b-apostate" \
        --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": "DreamFast/Qwen-2.5-7b-apostate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

DreamFast commited on 22 days ago

Commit

acd9982

verified ·

1 Parent(s): 02bb206

Add Discord community link

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README.md +3 -0

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@@ -16,6 +16,9 @@ library_name: transformers
 # Qwen2.5-7B-Instruct-Apostate
 An uncensored variant of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) produced with [Apostate](https://github.com/heterodoxin/apostate), `balanced` profile. Safety refusal behaviour removed via orthogonal projection of the refusal direction across 27 of 28 layers (skips layer 11).
 **7.61B parameters, bfloat16, single-shard safetensors (~15 GB).** No quantization. 55 of 339 tensors changed (35.8% of parameters). Targets `o_proj` + `down_proj` with a minimal embedding edit.

 # Qwen2.5-7B-Instruct-Apostate
+💬 **Community:** Join the [Abliterlitics Discord](https://discord.gg/AqmDnBjPvM) for discussion, model releases and support.
 An uncensored variant of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) produced with [Apostate](https://github.com/heterodoxin/apostate), `balanced` profile. Safety refusal behaviour removed via orthogonal projection of the refusal direction across 27 of 28 layers (skips layer 11).
 **7.61B parameters, bfloat16, single-shard safetensors (~15 GB).** No quantization. 55 of 339 tensors changed (35.8% of parameters). Targets `o_proj` + `down_proj` with a minimal embedding edit.