Instructions to use aitf-its-tim3-dfk/ministral-8b-merged-ws3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aitf-its-tim3-dfk/ministral-8b-merged-ws3 with Transformers:

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

pipe = pipeline("text-generation", model="aitf-its-tim3-dfk/ministral-8b-merged-ws3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("aitf-its-tim3-dfk/ministral-8b-merged-ws3")
model = AutoModelForMultimodalLM.from_pretrained("aitf-its-tim3-dfk/ministral-8b-merged-ws3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aitf-its-tim3-dfk/ministral-8b-merged-ws3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aitf-its-tim3-dfk/ministral-8b-merged-ws3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aitf-its-tim3-dfk/ministral-8b-merged-ws3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aitf-its-tim3-dfk/ministral-8b-merged-ws3

SGLang

How to use aitf-its-tim3-dfk/ministral-8b-merged-ws3 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 "aitf-its-tim3-dfk/ministral-8b-merged-ws3" \
    --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": "aitf-its-tim3-dfk/ministral-8b-merged-ws3",
		"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 "aitf-its-tim3-dfk/ministral-8b-merged-ws3" \
        --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": "aitf-its-tim3-dfk/ministral-8b-merged-ws3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aitf-its-tim3-dfk/ministral-8b-merged-ws3 with Docker Model Runner:
```
docker model run hf.co/aitf-its-tim3-dfk/ministral-8b-merged-ws3
```

ministral-8b-merged-ws3 / metrics.json

spuuntries

feat: card, template, metrics

d9a2b9f 29 days ago

Raw

History Blame Contribute Delete

1.27 kB

	{
	"models": {
	"DFK-3_multimodal": {
	"vlm_gen/cls_accuracy": 0.925,
	"vlm_gen/cls_f1_weighted": 0.9231573444047207,
	"vlm_gen/bertscore_f1": 0.8003071546554565,
	"vlm_gen/rouge_l_f1": 0.387058103281244,
	"text_gen/cls_accuracy": 0.775,
	"text_gen/cls_f1_weighted": 0.7083681876531683,
	"text_gen/bertscore_f1": 0.730573296546936,
	"text_gen/rouge_l_f1": 0.18964804860834042
	},
	"DFK-1_text": {
	"vlm_gen/cls_accuracy": 0.015,
	"vlm_gen/cls_f1_weighted": 0.028181818181818183,
	"vlm_gen/bertscore_f1": 0.6958135962486267,
	"vlm_gen/rouge_l_f1": 0.16208120267774412,
	"text_gen/cls_accuracy": 0.84,
	"text_gen/cls_f1_weighted": 0.889809384164223,
	"text_gen/bertscore_f1": 0.8070414662361145,
	"text_gen/rouge_l_f1": 0.4163341020470059
	},
	"Best_SLERP_Blend_w_0.30_mm_0.70_text": {
	"vlm_gen/cls_accuracy": 0.885,
	"vlm_gen/cls_f1_weighted": 0.8943747841713945,
	"vlm_gen/bertscore_f1": 0.773254930973053,
	"vlm_gen/rouge_l_f1": 0.3163897617151505,
	"text_gen/cls_accuracy": 0.91,
	"text_gen/cls_f1_weighted": 0.9288329276209023,
	"text_gen/bertscore_f1": 0.7756227254867554,
	"text_gen/rouge_l_f1": 0.3150810618299241
	}
	}
	}