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GaviZhou
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Model Details
Model Provider
GaviZhou
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Use this model for research and development workflows that compare a rendered or target figure with candidate code. The desired response format is:
json
{ "errors": [ { "category": "error category", "description": "description of the discrepancy", "code_span": { "start_line": 12, "end_line": 14, "anchor": "relevant code fragment" } } ]}code_span is optional. Line numbers are 1-based and should refer to the
candidate code supplied in the prompt.
Qwen/Qwen3-VL-8B-Instructtuner_type=full)1e-5, weight decay 0.01loss_scale=last_round)The checkpoint published here is the best checkpoint from the first training
run: checkpoint-198.
Teacher-forced validation metrics at checkpoint-198:
| Metric | Value |
|---|---|
eval_loss | 0.352678 |
eval_token_acc | 0.900092 |
The packed validation set contains 42 sequences. These are likelihood and token-level metrics, not direct measurements of error-detection quality or hallucination rate. For model selection or production use, additionally evaluate generated answers on held-out examples with JSON/schema validity, error-category precision/recall/F1, and code-anchor/line-alignment metrics.
Install a recent Transformers release with Qwen3-VL support, then load the model as an image-text-to-text model:
python
import torchfrom transformers import AutoProcessor, Qwen3VLForConditionalGeneration model_id = "GaviZhou/qwen3-vl-8b-i2c-hallu-v6-32k" model = Qwen3VLForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained(model_id) candidate_code = """# Candidate plotting code; retain 1-based line references."""messages = [ { "role": "user", "content": [ {"type": "image", "image": "/path/to/ground_truth.png"}, { "type": "text", "text": ( "Compare the ground-truth image with the candidate code. " "Return only the required JSON object.\n\n" f"Candidate code:\n{candidate_code}" ), }, ], }] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt",).to(model.device) generated_ids = model.generate(**inputs, max_new_tokens=1024)generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)]print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])The upstream Qwen3-VL model card contains additional examples and deployment guidance.
This checkpoint is derived from Qwen/Qwen3-VL-8B-Instruct, which is released
under the Apache-2.0 license. Users must also ensure that their use of the
fine-tuning data and downstream inputs is permitted.
If you use this model, please also cite the base model:
bibtex
@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}}{ "errors": [ { "category": "error category", "description": "description of the discrepancy", "code_span": { "start_line": 12, "end_line": 14, "anchor": "relevant code fragment" } } ]}import torchfrom transformers import AutoProcessor, Qwen3VLForConditionalGeneration model_id = "GaviZhou/qwen3-vl-8b-i2c-hallu-v6-32k" model = Qwen3VLForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained(model_id) candidate_code = """# Candidate plotting code; retain 1-based line references."""messages = [ { "role": "user", "content": [ {"type": "image", "image": "/path/to/ground_truth.png"}, { "type": "text", "text": ( "Compare the ground-truth image with the candidate code. " "Return only the required JSON object.\n\n" f"Candidate code:\n{candidate_code}" ), }, ], }] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt",).to(model.device) generated_ids = model.generate(**inputs, max_new_tokens=1024)generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)]print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}}