Instructions to use Zeineb12/evaluation-artifacts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zeineb12/evaluation-artifacts with Transformers:

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

pipe = pipeline("text-generation", model="Zeineb12/evaluation-artifacts")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zeineb12/evaluation-artifacts")
model = AutoModelForCausalLM.from_pretrained("Zeineb12/evaluation-artifacts", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zeineb12/evaluation-artifacts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zeineb12/evaluation-artifacts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zeineb12/evaluation-artifacts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zeineb12/evaluation-artifacts

SGLang

How to use Zeineb12/evaluation-artifacts 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 "Zeineb12/evaluation-artifacts" \
    --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": "Zeineb12/evaluation-artifacts",
		"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 "Zeineb12/evaluation-artifacts" \
        --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": "Zeineb12/evaluation-artifacts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zeineb12/evaluation-artifacts with Docker Model Runner:
```
docker model run hf.co/Zeineb12/evaluation-artifacts
```

Zeineb12 commited on 12 days ago

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@@ -14,7 +14,7 @@ tags:
 ---
-## Q1 — The Blind Spot
 I noticed that most AI companies announcing that their models discovered zero-day vulnerabilities rarely prove the real truth of their claims — even when they involve an independent third party. There is a difference between saying *"our model alone, unaided, discovered this zero-day"* and *"some engineer in the background noticed something and told the model to look for this zero-day."* Most companies honestly prove that the vulnerability is a zero-day because it was never found before — but they do not actually prove that their model, alone and without humans leaking it hints, was able to discover it. This is exactly why I chose this small example.
@@ -65,7 +65,7 @@ Both false justifications were written with the same fluent confidence as the mo
 ---
-## Q3 — A Path Forward
 My results show the model fails because it judges code by its *visual surface*, not by its *logic*: it reacts to how the code looks rather than tracing what the data actually does. So my proposed direction is a **data curation and fine-tuning strategy built on look-alike, different-meaning examples.**

 ---
+## Q1 : The Blind Spot
 I noticed that most AI companies announcing that their models discovered zero-day vulnerabilities rarely prove the real truth of their claims — even when they involve an independent third party. There is a difference between saying *"our model alone, unaided, discovered this zero-day"* and *"some engineer in the background noticed something and told the model to look for this zero-day."* Most companies honestly prove that the vulnerability is a zero-day because it was never found before — but they do not actually prove that their model, alone and without humans leaking it hints, was able to discover it. This is exactly why I chose this small example.
 ---
+## Q3 : A Path Forward
 My results show the model fails because it judges code by its *visual surface*, not by its *logic*: it reacts to how the code looks rather than tracing what the data actually does. So my proposed direction is a **data curation and fine-tuning strategy built on look-alike, different-meaning examples.**