Instructions to use nraesalmi/phi_3.5_pentest_eval2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nraesalmi/phi_3.5_pentest_eval2 with Transformers:

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

pipe = pipeline("text-generation", model="nraesalmi/phi_3.5_pentest_eval2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nraesalmi/phi_3.5_pentest_eval2")
model = AutoModelForCausalLM.from_pretrained("nraesalmi/phi_3.5_pentest_eval2", 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 nraesalmi/phi_3.5_pentest_eval2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nraesalmi/phi_3.5_pentest_eval2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nraesalmi/phi_3.5_pentest_eval2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nraesalmi/phi_3.5_pentest_eval2

SGLang

How to use nraesalmi/phi_3.5_pentest_eval2 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 "nraesalmi/phi_3.5_pentest_eval2" \
    --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": "nraesalmi/phi_3.5_pentest_eval2",
		"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 "nraesalmi/phi_3.5_pentest_eval2" \
        --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": "nraesalmi/phi_3.5_pentest_eval2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use nraesalmi/phi_3.5_pentest_eval2 with Docker Model Runner:
```
docker model run hf.co/nraesalmi/phi_3.5_pentest_eval2
```

nraesalmi commited on Jan 28

Commit

1b6c71e

verified ·

1 Parent(s): 8ee7c9b

(Trained with Unsloth)

Browse files

Files changed (9) hide show

added_tokens.json +13 -0
chat_template.jinja +15 -0
config.json +150 -0
generation_config.json +10 -0
model.safetensors +3 -0
special_tokens_map.json +30 -0
tokenizer.json +0 -0
tokenizer.model +3 -0
tokenizer_config.json +132 -0

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config.json ADDED Viewed

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+}

generation_config.json ADDED Viewed

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+  "max_length": 131072,
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+}

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+size 2264298245

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The diff for this file is too large to render. See raw diff

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+  "padding_side": "left",
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+  "tokenizer_class": "LlamaTokenizer",
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+  "use_default_system_prompt": false,
+  "chat_template": "{% if 'role' in messages[0] %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% else %}{{'<|' + message['role'] + '|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% else %}{% for message in messages %}{% if message['role'] == 'role' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'role' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% else %}{{'<|' + message['role'] + '|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% endif %}"
+}