Instructions to use protecttors/sample-files with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use protecttors/sample-files with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="protecttors/sample-files",
	filename="gguf_diffusion_model/retr0reg.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use protecttors/sample-files with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf protecttors/sample-files
# Run inference directly in the terminal:
llama-cli -hf protecttors/sample-files

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf protecttors/sample-files
# Run inference directly in the terminal:
llama-cli -hf protecttors/sample-files

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf protecttors/sample-files
# Run inference directly in the terminal:
./llama-cli -hf protecttors/sample-files

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf protecttors/sample-files
# Run inference directly in the terminal:
./build/bin/llama-cli -hf protecttors/sample-files

Use Docker

docker model run hf.co/protecttors/sample-files

LM Studio
Jan

vLLM

How to use protecttors/sample-files with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/protecttors/sample-files

Ollama
How to use protecttors/sample-files with Ollama:
```
ollama run hf.co/protecttors/sample-files
```

Unsloth Studio new

How to use protecttors/sample-files with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protecttors/sample-files to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protecttors/sample-files to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for protecttors/sample-files to start chatting

Docker Model Runner
How to use protecttors/sample-files with Docker Model Runner:
```
docker model run hf.co/protecttors/sample-files
```

Lemonade

How to use protecttors/sample-files with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull protecttors/sample-files

Run and chat with the model

lemonade run user.sample-files-{{QUANT_TAG}}

List all available models

lemonade list

protecttors commited on 14 days ago

Commit

07fc7dd

verified ·

1 Parent(s): 2ae8427

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -52

README.md CHANGED Viewed

@@ -143,18 +143,6 @@ Not applicable.
 #### Training Hyperparameters
 - **Training regime:** Not applicable — no training was performed by the Protecttors organization for this repository.
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-| Artifact Directory | Approximate Size | Format |
-|---|---|---|
-| `gguf_diffusion_model/` | ~280 MB | GGUF |
-| `ml_pkl_file/` | Variable | Python Pickle |
-| `torch_bin_model/` | Variable | PyTorch Binary |
-| **Total repository** | **~301 MB** | Mixed |
 ---
 ## Evaluation
@@ -165,14 +153,10 @@ Not applicable.
 #### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
 This repository is itself a test dataset for security tooling. It is not evaluated on NLP benchmarks.
 #### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 The relevant evaluation factors for tooling using this repository are:
 - **Format coverage:** Does the scanner/AIBOM tool correctly handle all three artifact formats?
@@ -203,8 +187,6 @@ This repository provides 4 unsafe artifacts and a mix of format types to stress-
 ## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
 The GGUF weights are derived from Qwen2-0.5B, a transformer-based autoregressive language model. No interpretability analysis has been performed by the Protecttors organization on these artifacts. Researchers wishing to inspect model internals may use GGUF header parsing tools to examine quantization metadata without loading full weights into memory.
 ---
@@ -250,42 +232,13 @@ Not applicable.
 - Pickle artifacts: Python 3.x standard library
 - PyTorch Package
----
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
----
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-| Term | Definition |
-|---|---|
-| **AIBOM** | AI Bill of Materials — a structured inventory of components, dependencies, and metadata for an AI model artifact |
-| **SBOM** | Software Bill of Materials — analogous to AIBOM but covering software supply chains broadly |
-| **VEX** | Vulnerability Exploitability eXchange — a document format for attaching exploitability status to known vulnerabilities in software/AI components |
-| **GGUF** | A binary serialization format for quantized LLM weights, used by `llama.cpp` and compatible runtimes |
-| **Pickle** | Python's native object serialization format; dangerous when loading untrusted sources as it supports arbitrary code execution |
-| **SafeTensors** | A safer alternative serialization format for ML weights that does not support code execution on load |
-| **Red teaming** | Adversarial testing methodology where security researchers simulate attacker behavior to identify vulnerabilities |
-| **CERT-In** | Indian Computer Emergency Response Team — the national nodal agency for cybersecurity incident response in India |
----
-## More Information [optional]
----
-## Model Card Authors [optional]
-Protecttors organization
----
 ## Model Card Contact

 #### Training Hyperparameters
 - **Training regime:** Not applicable — no training was performed by the Protecttors organization for this repository.
 ---
 ## Evaluation
 #### Testing Data
 This repository is itself a test dataset for security tooling. It is not evaluated on NLP benchmarks.
 #### Factors
 The relevant evaluation factors for tooling using this repository are:
 - **Format coverage:** Does the scanner/AIBOM tool correctly handle all three artifact formats?
 ## Model Examination [optional]
 The GGUF weights are derived from Qwen2-0.5B, a transformer-based autoregressive language model. No interpretability analysis has been performed by the Protecttors organization on these artifacts. Researchers wishing to inspect model internals may use GGUF header parsing tools to examine quantization metadata without loading full weights into memory.
 ---
 - Pickle artifacts: Python 3.x standard library
 - PyTorch Package
+### Framework versions
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3
 ## Model Card Contact