Instructions to use abhaybhargav/PWNISMS-Threat-Model-Structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("abhaybhargav/PWNISMS-Threat-Model-Structured")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="abhaybhargav/PWNISMS-Threat-Model-Structured",
	filename="PWNISMS-Threat-Model-Structured-F16.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 abhaybhargav/PWNISMS-Threat-Model-Structured with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf abhaybhargav/PWNISMS-Threat-Model-Structured:F16
# Run inference directly in the terminal:
llama-cli -hf abhaybhargav/PWNISMS-Threat-Model-Structured:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf abhaybhargav/PWNISMS-Threat-Model-Structured:F16
# Run inference directly in the terminal:
llama-cli -hf abhaybhargav/PWNISMS-Threat-Model-Structured:F16

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 abhaybhargav/PWNISMS-Threat-Model-Structured:F16
# Run inference directly in the terminal:
./llama-cli -hf abhaybhargav/PWNISMS-Threat-Model-Structured:F16

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 abhaybhargav/PWNISMS-Threat-Model-Structured:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abhaybhargav/PWNISMS-Threat-Model-Structured:F16

Use Docker

docker model run hf.co/abhaybhargav/PWNISMS-Threat-Model-Structured:F16

LM Studio
Jan

vLLM

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/abhaybhargav/PWNISMS-Threat-Model-Structured:F16

Ollama
How to use abhaybhargav/PWNISMS-Threat-Model-Structured with Ollama:
```
ollama run hf.co/abhaybhargav/PWNISMS-Threat-Model-Structured:F16
```

Unsloth Studio new

How to use abhaybhargav/PWNISMS-Threat-Model-Structured 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 abhaybhargav/PWNISMS-Threat-Model-Structured 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 abhaybhargav/PWNISMS-Threat-Model-Structured to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for abhaybhargav/PWNISMS-Threat-Model-Structured to start chatting

Pi new

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abhaybhargav/PWNISMS-Threat-Model-Structured"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "abhaybhargav/PWNISMS-Threat-Model-Structured"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abhaybhargav/PWNISMS-Threat-Model-Structured"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default abhaybhargav/PWNISMS-Threat-Model-Structured

Run Hermes

hermes

MLX LM

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "abhaybhargav/PWNISMS-Threat-Model-Structured"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "abhaybhargav/PWNISMS-Threat-Model-Structured"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "abhaybhargav/PWNISMS-Threat-Model-Structured",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use abhaybhargav/PWNISMS-Threat-Model-Structured with Docker Model Runner:
```
docker model run hf.co/abhaybhargav/PWNISMS-Threat-Model-Structured:F16
```

Lemonade

How to use abhaybhargav/PWNISMS-Threat-Model-Structured with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abhaybhargav/PWNISMS-Threat-Model-Structured:F16

Run and chat with the model

lemonade run user.PWNISMS-Threat-Model-Structured-F16

List all available models

lemonade list

PWNISMS-Threat-Model-Structured / examples

Commit History

Add fused MLX model weights and tokenizer

c380409
verified

abhaybhargav commited on 21 days ago

Commit History

Add fused MLX model weights and tokenizer c380409 verified

Add fused MLX model weights and tokenizer

c380409
verified