Instructions to use htlou/mm-interp-AA_preference_cocour_new_step10_0_70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use htlou/mm-interp-AA_preference_cocour_new_step10_0_70 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_preference_cocour_new_step10_0_70")
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_preference_cocour_new_step10_0_70")
model = AutoModelForMultimodalLM.from_pretrained("htlou/mm-interp-AA_preference_cocour_new_step10_0_70", 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_preference_cocour_new_step10_0_70 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_preference_cocour_new_step10_0_70"
# 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_preference_cocour_new_step10_0_70",
		"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_preference_cocour_new_step10_0_70

SGLang

How to use htlou/mm-interp-AA_preference_cocour_new_step10_0_70 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_preference_cocour_new_step10_0_70" \
    --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_preference_cocour_new_step10_0_70",
		"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_preference_cocour_new_step10_0_70" \
        --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_preference_cocour_new_step10_0_70",
		"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_preference_cocour_new_step10_0_70 with Docker Model Runner:
```
docker model run hf.co/htlou/mm-interp-AA_preference_cocour_new_step10_0_70
```

AA_preference_cocour_new_step10_0_70

This model is a fine-tuned version of llava-hf/llava-v1.6-mistral-7b-hf on the AA_preference_cocour_new_step10_0_70 dataset. It achieves the following results on the evaluation set:

Loss: 0.5221
Rewards/chosen: 0.8511
Rewards/rejected: -1.7632
Rewards/accuracies: 0.7827
Rewards/margins: 2.6143
Logps/rejected: -233.5941
Logps/chosen: -246.8883
Logits/rejected: -2.2402
Logits/chosen: -2.2674

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
0.5857	0.5348	50	0.5588	0.8626	-0.4673	0.7202	1.3299	-220.6345	-246.7726	-2.3950	-2.4070
0.2515	1.0695	100	0.5002	1.1633	-0.7568	0.7827	1.9201	-223.5298	-243.7656	-2.3802	-2.3927
0.2749	1.6043	150	0.5163	0.8806	-1.4497	0.7738	2.3303	-230.4583	-246.5927	-2.4269	-2.4377
0.148	2.1390	200	0.5244	1.0476	-1.4591	0.7887	2.5067	-230.5529	-244.9229	-2.2151	-2.2425
0.1437	2.6738	250	0.5224	0.8628	-1.7441	0.7798	2.6069	-233.4026	-246.7709	-2.2343	-2.2617

Framework versions

Transformers 4.45.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.20.3

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for htlou/mm-interp-AA_preference_cocour_new_step10_0_70

Base model

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

Finetuned

(189)

this model