Instructions to use rbryant19/opscribe-tool-recognition-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbryant19/opscribe-tool-recognition-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct")
model = PeftModel.from_pretrained(base_model, "rbryant19/opscribe-tool-recognition-adapter")

Transformers

How to use rbryant19/opscribe-tool-recognition-adapter with Transformers:

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

pipe = pipeline("text-generation", model="rbryant19/opscribe-tool-recognition-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbryant19/opscribe-tool-recognition-adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbryant19/opscribe-tool-recognition-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbryant19/opscribe-tool-recognition-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbryant19/opscribe-tool-recognition-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbryant19/opscribe-tool-recognition-adapter

SGLang

How to use rbryant19/opscribe-tool-recognition-adapter 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 "rbryant19/opscribe-tool-recognition-adapter" \
    --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": "rbryant19/opscribe-tool-recognition-adapter",
		"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 "rbryant19/opscribe-tool-recognition-adapter" \
        --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": "rbryant19/opscribe-tool-recognition-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbryant19/opscribe-tool-recognition-adapter with Docker Model Runner:
```
docker model run hf.co/rbryant19/opscribe-tool-recognition-adapter
```

opscribe-tool-recognition-adapter / processor_config.json

rbryant19

Tool recognition LoRA adapter — 3 epochs, 923 frames, loss 1.27→0.96

40cd25c verified about 1 month ago

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654 Bytes

	{
	"image_processor": {
	"do_convert_rgb": true,
	"do_normalize": true,
	"do_rescale": true,
	"do_resize": true,
	"image_mean": [
	0.48145466,
	0.4578275,
	0.40821073
	],
	"image_processor_type": "Qwen2VLImageProcessor",
	"image_std": [
	0.26862954,
	0.26130258,
	0.27577711
	],
	"max_pixels": 12845056,
	"merge_size": 2,
	"min_pixels": 3136,
	"patch_size": 14,
	"resample": 3,
	"rescale_factor": 0.00392156862745098,
	"size": {
	"longest_edge": 12845056,
	"shortest_edge": 3136
	},
	"temporal_patch_size": 2
	},
	"processor_class": "Qwen2_5_VLProcessor"
	}