Instructions to use aqweteddy/ministral-tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aqweteddy/ministral-tmp with Transformers:

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

pipe = pipeline("image-text-to-text", model="aqweteddy/ministral-tmp")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("aqweteddy/ministral-tmp")
model = AutoModelForImageTextToText.from_pretrained("aqweteddy/ministral-tmp")
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

vLLM

How to use aqweteddy/ministral-tmp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aqweteddy/ministral-tmp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aqweteddy/ministral-tmp",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/aqweteddy/ministral-tmp

SGLang

How to use aqweteddy/ministral-tmp 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 "aqweteddy/ministral-tmp" \
    --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": "aqweteddy/ministral-tmp",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "aqweteddy/ministral-tmp" \
        --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": "aqweteddy/ministral-tmp",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use aqweteddy/ministral-tmp with Docker Model Runner:
```
docker model run hf.co/aqweteddy/ministral-tmp
```

ministral-3-3b-base_cp-v2.0.0-s0e2_cv-reasoning

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Task Arithmetic merge method using /home/pinzuli/models/ministral/Ministral-3-3B-Base-2512/ as a base.

Models Merged

The following models were included in the merge:

/home/pinzuli/swift_trainer/output/ministral-3-3b-base_cp-v2.0.0
/home/pinzuli/models/ministral/Ministral-3-3B-Reasoning-2512

Configuration

The following YAML configuration was used to produce this model:

# Chat Vector Merge using Task Arithmetic
#
# This config applies the "chat vector" from an instruct model to a target model.
# The chat vector is computed as: chat_model - base_model
# Then applied as: target_model + weight * (chat_model - base_model)
#
# Since we want to apply the chat vector to our target model (checkpoint),
# we set the checkpoint as the base_model and only include the chat_model.
# The task vector (chat_model - base_model) will be computed and added back.

models:
  # The instruct/chat model that provides the "chat capabilities"
  - model: /home/pinzuli/models/ministral/Ministral-3-3B-Reasoning-2512
    parameters:
      weight:
        # 1.0
        - filter: embed_tokens
          value: 0
        - filter: lm_head
          value: 0
        - value: 1.0
  # The target model (your fine-tuned checkpoint)
  - model: /home/pinzuli/swift_trainer/output/ministral-3-3b-base_cp-v2.0.0
    parameters:
      weight: 1.0
merge_method: task_arithmetic
base_model: /home/pinzuli/models/ministral/Ministral-3-3B-Base-2512/
tokenizer_source: /home/pinzuli/models/ministral/Ministral-3-3B-Reasoning-2512
dtype: bfloat16

Downloads last month: 3

Safetensors

Model size

4B params

Tensor type

BF16

Paper for aqweteddy/ministral-tmp

Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 8