Instructions to use FelixKAI/UHR-BAT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FelixKAI/UHR-BAT2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="FelixKAI/UHR-BAT2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FelixKAI/UHR-BAT2")
model = AutoModelForMultimodalLM.from_pretrained("FelixKAI/UHR-BAT2", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FelixKAI/UHR-BAT2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FelixKAI/UHR-BAT2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FelixKAI/UHR-BAT2",
		"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/FelixKAI/UHR-BAT2

SGLang

How to use FelixKAI/UHR-BAT2 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 "FelixKAI/UHR-BAT2" \
    --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": "FelixKAI/UHR-BAT2",
		"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 "FelixKAI/UHR-BAT2" \
        --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": "FelixKAI/UHR-BAT2",
		"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 FelixKAI/UHR-BAT2 with Docker Model Runner:
```
docker model run hf.co/FelixKAI/UHR-BAT2
```

UHR-BAT2 / EVALUATION.md

FelixKAI

Duplicate from RL-MIND/UHR-BAT2

94c9578 8 days ago

preview code

Raw History Blame Contribute Delete

6.31 kB

UHR-BAT2 evaluation

English · 中文 · Model card

This document reports audited results for the qwen3vl-8b-geollava-ft3-selected-10k checkpoint released as UHR-BAT2. Existing prediction artifacts were checked on 2026-10-13; no new inference was run for this audit. Machine-readable counts, categories, protocol identifiers and source hashes are in evaluation_results.json.

Main results

Accuracy is sample-weighted unless stated otherwise. All three datasets use the corrected dense scoring protocol. Scores from different datasets should not be treated as interchangeable.

Dataset	Correct / Total	Accuracy	Scope
RSHR-Bench	980 / 1,932	50.72%	All QA
XLRS-Bench-lite	1,610 / 3,080	52.27%	Strict option-set accuracy
MME-RealWorld Remote Sensing	2,123 / 3,738	56.80%	Full remote-sensing subset

Denominators and answer formats

RSHR single-turn Overall is 726/1,590 = 45.66%. The remaining multi-turn QA records score 254/342 = 74.27%. The combined 50.72% score must not be labeled as the 1,590-record single-turn Overall metric. The multi-turn group is defined by the three original category identifiers multitrun-future_prediction, multiturn_object_state_judgement, and multturn_anomaly_detection.

XLRS contains 2,980 single-choice records: 1,563 correct (52.45%), and 100 multi-select task records: 47 correct (47.00%). Multi-select examples require an exact match of the complete canonical option set. The evaluated stored split is labeled train; this label describes the benchmark file, not evidence that those records were used for model training. MME evaluates all 3,738 Remote Sensing records, rather than a Lite subset.

Evaluation protocol

The corrected dense run uses 20260913_strict_sets_matched_schedule_v1, BF16 model weights, eager attention, min_pixels=784, and max_pixels=1,310,720. Dense means that visual tokens are not pruned after preprocessing. Images are still resized to the pixel budget; these results do not establish native 4K/8K or full-resolution ultra-high-resolution perception.

For single-choice questions, the evaluator selects the highest candidate-option logit using FP32 rows from the language-model output head. For XLRS multi-select questions, a constrained greedy token-trie decoder with FP32 candidate rows produces a canonical option set, which is scored by exact set matching.

The ordinary model.generate() quickstart is an inference demonstration, not a reproduction of these scores. Reproduction requires the same benchmark records, prompts, image processing, candidate-option scoring or constrained multi-select decoder, and answer normalization. Changing any of these may change the measured accuracy.

Category results

Original dataset category identifiers, including spelling differences, are retained for traceability. All category scores below use the protocol selected for that dataset in the main table.

RSHR-Bench

Category	Correct / Total	Accuracy
anomaly_detection	37 / 50	74.00%
color	135 / 200	67.50%
detection	32 / 100	32.00%
future_prediction_two_image	27 / 50	54.00%
multi_region_join_contrast	13 / 20	65.00%
multi_region_join_contrast_singal	22 / 50	44.00%
multitrun-future_prediction	100 / 150	66.67%
multiturn_object_state_judgement	112 / 132	84.85%
multturn_anomaly_detection	42 / 60	70.00%
object_clasfication	112 / 200	56.00%
object_counting	33 / 200	16.50%
object_grounding	74 / 200	37.00%
object_state_judgement	30 / 50	60.00%
regional_grouding	33 / 70	47.14%
regionla_counting	43 / 100	43.00%
relation	108 / 200	54.00%
shape	27 / 100	27.00%

XLRS-Bench-lite

Category	Correct / Total	Accuracy
Complex reasoning/Anomaly Detection and Interpretation	73 / 100	73.00%
Complex reasoning/Environmental condition reasoning	80 / 100	80.00%
Complex reasoning/Route planning	50 / 100	50.00%
Counting/Counting with changing detection	30 / 60	50.00%
Counting/Counting with complex reasoning	55 / 100	55.00%
Counting/Overall counting	22 / 60	36.67%
Counting/Regional counting	47 / 100	47.00%
Land use classification/Overall Land use classification	47 / 100	47.00%
Land use classification/Regional Land use classification	169 / 200	84.50%
Object properties/Object classification	422 / 800	52.75%
Object properties/Object color	374 / 800	46.75%
Object properties/Object motion state	39 / 60	65.00%
Object spatial relationship/Object spatial relationship	202 / 500	40.40%

MME-RealWorld Remote Sensing

Category	Correct / Total	Accuracy
color	855 / 1,255	68.13%
count	337 / 1,226	27.49%
position	931 / 1,257	74.07%

Verification and interpretation

All corrected dense prediction shards were read in full. Each dataset has complete, unique expected row indices; saved correctness agrees with answer/prediction equality, and protocol sidecars and completion markers are consistent. Recorded checkpoint, evaluator-code and protocol identities match across the corrected runs. Source artifact and evaluator hashes are provided in the JSON.

Excluded historical XLRS results: an earlier evaluator requested one option letter and truncated multi-option ground truths. Those results are not valid strict option-set accuracy and are excluded from this release report. No same-protocol accuracy gain over the original base model is claimed here.

The results show stronger performance on MME position and color questions and XLRS regional land-use classification, while counting is a recurring weakness, especially RSHR object counting and MME count. These are within-benchmark observations, not controlled cross-dataset or model comparisons.

Only aggregate results are included here. Benchmark images, questions and raw prediction files are not redistributed in this evaluation artifact. The hashes identify audited source files; the aggregate JSON alone is not a complete public reproduction package.