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")

Notebooks
Google Colab
Kaggle
Local Apps

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 29 days ago

Commit

012d1ec

verified ·

1 Parent(s): 9b753e8

Final landing: 266K parameters trained on i5-10210U

Browse files

Files changed (5) hide show

README.md +51 -0
config.json +34 -0
generation_config.json +9 -0
model.safetensors +3 -0
training_args.bin +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,51 @@

+---
+library_name: transformers
+tags:
+- generated_from_trainer
+model-index:
+- name: dragon_interceptor
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# dragon_interceptor
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0005
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+### Training results
+### Framework versions
+- Transformers 5.0.0
+- Pytorch 2.10.0+cpu
+- Datasets 4.8.4
+- Tokenizers 0.22.2

config.json ADDED Viewed

	@@ -0,0 +1,34 @@

+{
+  "activation_function": "gelu_new",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 255,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_embd": 64,
+  "n_head": 4,
+  "n_inner": null,
+  "n_layer": 4,
+  "n_positions": 784,
+  "pad_token_id": null,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "vocab_size": 256
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,9 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 255,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.0.0",
+  "use_cache": true
+}

model.safetensors ADDED Viewed

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+size 1071392

training_args.bin ADDED Viewed

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