Instructions to use MTGxTHOR/V115_SFT_Outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MTGxTHOR/V115_SFT_Outputs with Transformers:

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

pipe = pipeline("image-text-to-text", model="MTGxTHOR/V115_SFT_Outputs")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MTGxTHOR/V115_SFT_Outputs")
model = AutoModelForMultimodalLM.from_pretrained("MTGxTHOR/V115_SFT_Outputs", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MTGxTHOR/V115_SFT_Outputs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MTGxTHOR/V115_SFT_Outputs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MTGxTHOR/V115_SFT_Outputs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MTGxTHOR/V115_SFT_Outputs

SGLang

How to use MTGxTHOR/V115_SFT_Outputs 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 "MTGxTHOR/V115_SFT_Outputs" \
    --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": "MTGxTHOR/V115_SFT_Outputs",
		"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 "MTGxTHOR/V115_SFT_Outputs" \
        --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": "MTGxTHOR/V115_SFT_Outputs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MTGxTHOR/V115_SFT_Outputs with Docker Model Runner:
```
docker model run hf.co/MTGxTHOR/V115_SFT_Outputs
```

MTGxTHOR commited on 15 days ago

Commit

12cbb84

verified ·

1 Parent(s): 304dfad

qwen2.5-vl-3b-plant-pathology-sft

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README.md +1 -1

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
 # V115_SFT_Outputs
-This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) on the None dataset.
 ## Model description

 # V115_SFT_Outputs
+This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) on an unknown dataset.
 ## Model description