pianzhikuang

VisAlloc_New

README

License: apache-2.0

Usage

The evaluation environment used Transformers 5.5.0, PyTorch 2.10.0, and vLLM 0.18.0. Keep the supplied processor and perception chat template.

python

import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "pianzhikuang/VisAlloc_New"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained(    model_id, dtype=torch.bfloat16, device_map="auto").eval() image = Image.open("image.jpg").convert("RGB")messages = [{"role": "user", "content": [    {"type": "image", "image": image},    {"type": "text", "text": "Describe this image."},]}]inputs = processor.apply_chat_template(    messages, tokenize=True, add_generation_prompt=True,    return_dict=True, return_tensors="pt",).to(model.device)with torch.inference_mode():    output = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(processor.decode(    output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Evaluation

The following results were measured on this checkpoint on October 11, 2026. Inference followed the repository README: full benchmark inputs, BF16, greedy decoding (temperature=0), seed 42, up to 32,768 generated tokens, and the model's native 262,144-token context. The supplied perception template was used without an enable_thinking override.

Judging used exact matching for unambiguous answers and GPT-6 Astra for semantic equivalence decisions. This judge differs from the README's gpt-oss-120b example, so comparisons should account for that protocol difference. All 16,074 predictions completed, with no API-error records, empty answers, or generated </think> tags.

Table with columns: Benchmark, Metric, Score (%), Samples
Benchmark	Metric	Score (%)	Samples
V* Bench	Accuracy	86.91	191
ZoomBench	Reference-match rate*	63.55	845
HR-Bench 4K	Accuracy	85.50	800
HR-Bench 8K	Accuracy	83.38	800

*ZoomBench contains one invalid reference: a question supplies only A/B choices but its reference label is D. That record remains unscorable. The table retains the repository's full 845-row denominator, giving 537/845 = 63.55% reference matches; accuracy on the 844 valid-reference samples is 537/844 = 63.63%. This annotation problem is recorded in the results and should not be interpreted as a confirmed model error.

See results.json, results.csv, and the evaluation manifest for exact counts, additional metrics, generation settings, and integrity hashes.

Release integrity

model.safetensors is the exact merged weight file used in this evaluation:

text

041a210644ce7f5d047cbefdbd8a087c2206fc9b799192f5ee6d397ea974d3f9

The perception template SHA-256 is:

text

d9604b52b4e1f4b9ec68e065238c757a3d7efdebe1c3692d13a97df6f84c54db

The published top-level configuration explicitly declares bfloat16 to match the saved weights. Weights and chat template are unchanged from evaluation.

The base model's Apache-2.0 license is included in LICENSE. Training images and benchmark images are separate resources and are not part of this model release.

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Model Details

Model Provider

pianzhikuang

Model Tree

Base

Qwen/Qwen3.5-9B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

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README

License: apache-2.0

Usage

The evaluation environment used Transformers 5.5.0, PyTorch 2.10.0, and vLLM 0.18.0. Keep the supplied processor and perception chat template.

python

import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "pianzhikuang/VisAlloc_New"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained(    model_id, dtype=torch.bfloat16, device_map="auto").eval() image = Image.open("image.jpg").convert("RGB")messages = [{"role": "user", "content": [    {"type": "image", "image": image},    {"type": "text", "text": "Describe this image."},]}]inputs = processor.apply_chat_template(    messages, tokenize=True, add_generation_prompt=True,    return_dict=True, return_tensors="pt",).to(model.device)with torch.inference_mode():    output = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(processor.decode(    output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Evaluation

Table with columns: Benchmark, Metric, Score (%), Samples
Benchmark	Metric	Score (%)	Samples
V* Bench	Accuracy	86.91	191
ZoomBench	Reference-match rate*	63.55	845
HR-Bench 4K	Accuracy	85.50	800
HR-Bench 8K	Accuracy	83.38	800

See results.json, results.csv, and the evaluation manifest for exact counts, additional metrics, generation settings, and integrity hashes.

Release integrity

model.safetensors is the exact merged weight file used in this evaluation:

text

041a210644ce7f5d047cbefdbd8a087c2206fc9b799192f5ee6d397ea974d3f9

The perception template SHA-256 is:

text

d9604b52b4e1f4b9ec68e065238c757a3d7efdebe1c3692d13a97df6f84c54db

The published top-level configuration explicitly declares bfloat16 to match the saved weights. Weights and chat template are unchanged from evaluation.

The base model's Apache-2.0 license is included in LICENSE. Training images and benchmark images are separate resources and are not part of this model release.