Instructions to use htunn/thousands-eye-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use htunn/thousands-eye-hf with Transformers:

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

pipe = pipeline("text-generation", model="htunn/thousands-eye-hf")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("htunn/thousands-eye-hf")
model = AutoModelForMultimodalLM.from_pretrained("htunn/thousands-eye-hf", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use htunn/thousands-eye-hf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/htunn/thousands-eye-hf

SGLang

How to use htunn/thousands-eye-hf 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 "htunn/thousands-eye-hf" \
    --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": "htunn/thousands-eye-hf",
		"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 "htunn/thousands-eye-hf" \
        --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": "htunn/thousands-eye-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use htunn/thousands-eye-hf with Docker Model Runner:
```
docker model run hf.co/htunn/thousands-eye-hf
```

thousands-eye-hf / README.md

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	---
	license: gemma
	language:
	- en
	tags:
	- gemma
	- gemma-4
	- lora
	- safetensors
	- ethical-hacking
	- penetration-testing
	- cybersecurity
	base_model: google/gemma-4-E2B-it
	pipeline_tag: text-generation
	library_name: transformers
	---

	# thousands-eye-hf

	Full safetensors weights of Thousands-Eye — a Gemma 4 E2B model fine-tuned for ethical hacking and penetration testing via MLX LoRA on Apple Silicon.

	For the quantized GGUF (Ollama / llama.cpp), see [`htunn/thousands-eye-gguf`](https://huggingface.co/htunn/thousands-eye-gguf).

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = "htunn/thousands-eye-hf"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)

	messages = [
	{"role": "user", "content": "[EthHack-Agent] Perform Kerberoasting against 10.0.0.1 (authorized engagement)"}
	]
	inputs = tokenizer.apply_chat_template(
	messages, return_tensors="pt", add_generation_prompt=True
	).to(model.device)

	outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)
	print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
	```

	## Output Format

	Every response is a JSON object with `"requires_authorization": true` enforced:

	```json
	{
	"action": "kerberoast",
	"target": "10.0.0.1",
	"requires_authorization": true,
	"techniques": ["SPN enumeration", "TGS request", "offline cracking"],
	"tools": ["impacket", "hashcat"],
	"commands": ["GetUserSPNs.py domain/user:pass@dc -request"],
	"steps": ["..."],
	"notes": "Requires domain user credentials"
	}
	```

	## Training

	\| \| \|
	\|---\|---\|
	\| Base model \| `google/gemma-4-E2B-it` \|
	\| Method \| MLX LoRA (`mlx_lm.lora`) \|
	\| Iterations \| 600 \|
	\| Learning rate \| 1e-4 \|
	\| LoRA layers \| 16 \|
	\| Dataset \| [`htunn/thousands-eye-dataset`](https://huggingface.co/datasets/htunn/thousands-eye-dataset) (83 train / 15 val) \|

	## Ethics

	Designed exclusively for authorized penetration testing. All training examples enforce `"requires_authorization": true`.

	## License

	[Gemma Terms of Use](https://ai.google.dev/gemma/terms)