Instructions to use Vadim21221/qwen2_1_5b-instruct-steering-vector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vadim21221/qwen2_1_5b-instruct-steering-vector with Transformers:

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

pipe = pipeline("text-generation", model="Vadim21221/qwen2_1_5b-instruct-steering-vector")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Vadim21221/qwen2_1_5b-instruct-steering-vector", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vadim21221/qwen2_1_5b-instruct-steering-vector with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vadim21221/qwen2_1_5b-instruct-steering-vector"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vadim21221/qwen2_1_5b-instruct-steering-vector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vadim21221/qwen2_1_5b-instruct-steering-vector

SGLang

How to use Vadim21221/qwen2_1_5b-instruct-steering-vector 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 "Vadim21221/qwen2_1_5b-instruct-steering-vector" \
    --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": "Vadim21221/qwen2_1_5b-instruct-steering-vector",
		"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 "Vadim21221/qwen2_1_5b-instruct-steering-vector" \
        --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": "Vadim21221/qwen2_1_5b-instruct-steering-vector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vadim21221/qwen2_1_5b-instruct-steering-vector with Docker Model Runner:
```
docker model run hf.co/Vadim21221/qwen2_1_5b-instruct-steering-vector
```

qwen2_1_5b-instruct-steering-vector

6.19 GB

Ctrl+K

1 contributor

History: 2 commits

Vadim21221

Training in progress, step 10

ccc47eb verified about 1 year ago

Vadim21221
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qwen2_1_5b-instruct-steering-vector

Detected Pickle imports (10)