Instructions to use NaughtyDog97/DiagramFormalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NaughtyDog97/DiagramFormalizer with Transformers:

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

pipe = pipeline("text-generation", model="NaughtyDog97/DiagramFormalizer", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("NaughtyDog97/DiagramFormalizer", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NaughtyDog97/DiagramFormalizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NaughtyDog97/DiagramFormalizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NaughtyDog97/DiagramFormalizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NaughtyDog97/DiagramFormalizer

SGLang

How to use NaughtyDog97/DiagramFormalizer 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 "NaughtyDog97/DiagramFormalizer" \
    --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": "NaughtyDog97/DiagramFormalizer",
		"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 "NaughtyDog97/DiagramFormalizer" \
        --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": "NaughtyDog97/DiagramFormalizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NaughtyDog97/DiagramFormalizer with Docker Model Runner:
```
docker model run hf.co/NaughtyDog97/DiagramFormalizer
```

NaughtyDog97 commited on Jul 30, 2024

Commit

752fa01

verified ·

1 Parent(s): ad3212f

Upload modeling_fegeo_qwen2.py

Browse files

Files changed (1) hide show

modeling_fegeo_qwen2.py +7 -3

modeling_fegeo_qwen2.py CHANGED Viewed

@@ -615,10 +615,14 @@ from .configuration_fegeo_qwen2 import Qwen2Config
 if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
 logger = logging.get_logger(__name__)

 if is_flash_attn_2_available():
+    try:
+        from flash_attn import flash_attn_func, flash_attn_varlen_func
+        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+        _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+    except:
+        flash_attn_func, flash_attn_varlen_func, index_first_axis, pad_input, unpad_input = None, None, None, None, None
 logger = logging.get_logger(__name__)