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

Libraries

How to use microsoft/HARC-Qwen2.5-7B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/HARC-Qwen2.5-7B-Instruct

SGLang

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

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

vincchua commited on Jul 2

Commit

2c66da9

verified ·

1 Parent(s): 163353c

Add model card (license + base_model)

Browse files

Files changed (1) hide show

README.md +23 -0

README.md ADDED Viewed

	@@ -0,0 +1,23 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+base_model_relation: merge
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- safety
+- alignment
+- jailbreak-robustness
+- harc
+---
+# HARC — Qwen2.5-7B-Instruct (merged)
+HARC safety-alignment LoRA merged into `Qwen/Qwen2.5-7B-Instruct` (full standalone model).
+Part of the [HARC](https://github.com/microsoft/HARC) release; see paper arXiv:2607.00572.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("microsoft/HARC-Qwen2.5-7B-Instruct", torch_dtype="bfloat16", device_map="auto")
+tok = AutoTokenizer.from_pretrained("microsoft/HARC-Qwen2.5-7B-Instruct")
+```