Instructions to use amy011872/LawToken-7B-a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amy011872/LawToken-7B-a2 with Transformers:

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

pipe = pipeline("text-generation", model="amy011872/LawToken-7B-a2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amy011872/LawToken-7B-a2")
model = AutoModelForCausalLM.from_pretrained("amy011872/LawToken-7B-a2", 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 amy011872/LawToken-7B-a2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amy011872/LawToken-7B-a2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amy011872/LawToken-7B-a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amy011872/LawToken-7B-a2

SGLang

How to use amy011872/LawToken-7B-a2 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 "amy011872/LawToken-7B-a2" \
    --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": "amy011872/LawToken-7B-a2",
		"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 "amy011872/LawToken-7B-a2" \
        --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": "amy011872/LawToken-7B-a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amy011872/LawToken-7B-a2 with Docker Model Runner:
```
docker model run hf.co/amy011872/LawToken-7B-a2
```

Potential Exposure of Hugging Face Token

by June-Snow - opened May 24, 2025

Discussion

June-Snow

May 24, 2025

Hello,

We are a group of security researchers conducting an analysis of publicly available resources to assess common misconfiguration risks.

During our research, we came across a Hugging Face access token exposed in a public configuration: hf_tEE******BUTpm. This token appears to be linked to your account and could potentially allow unauthorized access to Hugging Face services. If left unrevoked, it may be vulnerable to misuse.

We strongly recommend that you revoke this token immediately and generate a new one. Additionally, please review your configurations to ensure that no other sensitive credentials are publicly accessible.

If you have any questions or would like assistance in securing your environment, feel free to reach out.

Best regards,

June-Snow

May 24, 2025

As a follow-up, it looks like there’s another leaked token: hf_FBIV******CfQC.

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