Instructions to use htlou/mm-interp-AA_text_image_to_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use htlou/mm-interp-AA_text_image_to_text with Transformers:

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

pipe = pipeline("image-text-to-text", model="htlou/mm-interp-AA_text_image_to_text")
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("htlou/mm-interp-AA_text_image_to_text")
model = AutoModelForMultimodalLM.from_pretrained("htlou/mm-interp-AA_text_image_to_text", device_map="auto")
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 htlou/mm-interp-AA_text_image_to_text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "htlou/mm-interp-AA_text_image_to_text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "htlou/mm-interp-AA_text_image_to_text",
		"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/htlou/mm-interp-AA_text_image_to_text

SGLang

How to use htlou/mm-interp-AA_text_image_to_text 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 "htlou/mm-interp-AA_text_image_to_text" \
    --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": "htlou/mm-interp-AA_text_image_to_text",
		"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 "htlou/mm-interp-AA_text_image_to_text" \
        --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": "htlou/mm-interp-AA_text_image_to_text",
		"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 htlou/mm-interp-AA_text_image_to_text with Docker Model Runner:
```
docker model run hf.co/htlou/mm-interp-AA_text_image_to_text
```

AA_text_image_to_text

This model is a fine-tuned version of llava-hf/llava-v1.6-mistral-7b-hf on the AA_text_image_to_text dataset. It achieves the following results on the evaluation set:

Loss: 0.4500
Rewards/chosen: -0.6971
Rewards/rejected: -4.4006
Rewards/accuracies: 0.8206
Rewards/margins: 3.7035
Logps/rejected: -242.2139
Logps/chosen: -207.2900
Logits/rejected: -1.9132
Logits/chosen: -1.9735

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.4516	0.3623	50	0.4421	0.8113	-0.8305	0.7903	1.6418	-206.5130	-192.2066	-1.6310	-1.7066
0.3759	0.7246	100	0.4121	-0.0472	-2.2762	0.8145	2.2290	-220.9696	-200.7911	-1.7930	-1.8524
0.149	1.0870	150	0.4205	0.5835	-1.8816	0.8206	2.4651	-217.0244	-194.4847	-1.6746	-1.7425
0.1474	1.4493	200	0.4274	-0.5411	-3.7374	0.8306	3.1963	-235.5818	-205.7306	-1.7947	-1.8599
0.1268	1.8116	250	0.4333	-0.0670	-3.3107	0.8206	3.2437	-231.3154	-200.9896	-2.0993	-2.1450
0.064	2.1739	300	0.4332	-0.5167	-4.0958	0.8306	3.5792	-239.1665	-205.4860	-1.9327	-1.9909
0.056	2.5362	350	0.4481	-0.5224	-4.1134	0.8185	3.5910	-239.3422	-205.5439	-1.9163	-1.9756
0.0721	2.8986	400	0.4507	-0.7023	-4.4082	0.8185	3.7059	-242.2901	-207.3426	-1.9129	-1.9731

Framework versions

Transformers 4.45.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.20.3

Downloads last month: 2

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for htlou/mm-interp-AA_text_image_to_text

Base model

llava-hf/llava-v1.6-mistral-7b-hf

Finetuned

(189)

this model