Instructions to use MightyDragon-Dev/dragon_interceptor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MightyDragon-Dev/dragon_interceptor with Transformers:

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

pipe = pipeline("text-generation", model="MightyDragon-Dev/dragon_interceptor")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MightyDragon-Dev/dragon_interceptor")
model = AutoModelForCausalLM.from_pretrained("MightyDragon-Dev/dragon_interceptor", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MightyDragon-Dev/dragon_interceptor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MightyDragon-Dev/dragon_interceptor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MightyDragon-Dev/dragon_interceptor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MightyDragon-Dev/dragon_interceptor

SGLang

How to use MightyDragon-Dev/dragon_interceptor 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 "MightyDragon-Dev/dragon_interceptor" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MightyDragon-Dev/dragon_interceptor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MightyDragon-Dev/dragon_interceptor" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MightyDragon-Dev/dragon_interceptor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MightyDragon-Dev/dragon_interceptor with Docker Model Runner:
```
docker model run hf.co/MightyDragon-Dev/dragon_interceptor
```

MightyDragon-Dev commited on Apr 16

Commit

c091c99

verified ·

1 Parent(s): c65c850

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -50,12 +50,13 @@ Use the following code to generate a high-fidelity "Radar Scan" from a specific
 import torch
 from transformers import AutoModelForCausalLM
 import matplotlib.pyplot as plt
 # Load Dragon Interceptor
 model = AutoModelForCausalLM.from_pretrained("MightyDragon-Dev/dragon_interceptor")
-# Generate a 28x28 Blueprint (Seed 42)
-seed_id = 42
 input_ids = torch.tensor([[seed_id]])
 output = model.generate(input_ids, max_length=784, min_length=784, do_sample=True, temperature=0.7)
@@ -67,4 +68,4 @@ plt.imshow(blueprint, cmap='magma', interpolation='lanczos')
 plt.title(f"Dragon Interceptor: Sector Scan (Seed {seed_id})", color='white')
 plt.style.use('dark_background')
 plt.axis('off')
-plt.show()

 import torch
 from transformers import AutoModelForCausalLM
 import matplotlib.pyplot as plt
+import os
 # Load Dragon Interceptor
 model = AutoModelForCausalLM.from_pretrained("MightyDragon-Dev/dragon_interceptor")
+# Generate a 28x28 Blueprint (Random Seed)
+seed_id = os.urandom(1)[0] % 1000  # Random seed for variability
+print(f"🚀 Generating Dragon Blueprint with Seed {seed_id}...")
 input_ids = torch.tensor([[seed_id]])
 output = model.generate(input_ids, max_length=784, min_length=784, do_sample=True, temperature=0.7)
 plt.title(f"Dragon Interceptor: Sector Scan (Seed {seed_id})", color='white')
 plt.style.use('dark_background')
 plt.axis('off')
+plt.show()