Scalpel-VL-1.8B

Scalpel-VL-1.7B

Scalpel-VL-1.7B is a structurally pruned and recovery-trained vision-language model based on the Qwen3-VL-2B-Instruct architecture. It is the Round 7 post_recovery_model produced by Scalpel.

Seven language decoder layers were physically removed from the original 28-layer model. The remaining 21-layer student was recovered after every pruning round using a fixed reference teacher, final-logit knowledge distillation, and merged all-linear LoRA adapters.

The checkpoint contains 1,775,180,032 parameters. The repository name uses the parameter count rounded down to one decimal place: 1.7B.

Model details

The removed-layer list above records the current-to-original layer mapping for the seven completed pruning rounds.

Recovery protocol

At each round, Scalpel:

1. evaluates candidate language layers with a fixed text probe;
2. physically removes the lowest-risk current layer;
3. trains only all-linear LoRA parameters on the pruned student;
4. uses the fixed reference model as teacher and aligns final LM-head logits;
5. merges the adapter and uses the exported model as the next-round student.

Recovery used the approximately 0.1B-token ScalpelBench instruction-response mixture, covering English, Chinese, mathematical reasoning, and code. The experiment partitions the mixture into ten deterministic token-balanced parts; this Round 7 checkpoint has completed recovery on parts 1 through 7.

Internal evaluation

The following measurements use the complete 1,568-sample ScalpelBench validation split with 276,216 supervised tokens and a maximum sequence length of 1,536. Macro score is 100 times the macro average of teacher-forced token accuracy over English, Chinese, Math, and Code. It is an internal controlled metric, not an Open LLM Leaderboard score.

Under this evaluation setup, the pruned checkpoint uses 16.56% fewer parameters, has 25% fewer language layers, achieves 23.07% higher supervised token throughput, and reduces peak CUDA allocation by 20.93%. Throughput and memory numbers are hardware- and software-dependent and should not be treated as universal deployment guarantees.

Usage

Install a Transformers version that supports Qwen3-VL, then load the model and processor directly from the Hub:

import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model_id = "freeai-org/Scalpel-VL-1.7B"

model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"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():
    generated_ids = model.generate(**inputs, max_new_tokens=128)

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])

For lower-memory inference, select an attention implementation and dtype that are supported by your hardware. This repository contains merged full-model weights; no separate LoRA adapter is required.

Intended use and limitations

This checkpoint is intended for research on structured pruning, post-pruning recovery, knowledge distillation, and efficient multimodal inference.

• Removing decoder layers can change general reasoning, multilingual, OCR, grounding, long-context, and video behavior.
• The reported evaluation is teacher-forced and uses ScalpelBench; it does not establish performance on unrelated benchmarks or production traffic.
• ScalpelBench includes material derived from multiple upstream datasets. Review its dataset card and source licenses before downstream use.
• Model outputs may be incorrect, biased, or unsafe. Validate the checkpoint for the target domain before deployment, especially in high-stakes settings.

Related resources

• Scalpel code
• Scalpel project page
• ScalpelBench
• Qwen3-VL-2B-Instruct

Citation

@misc{wu2026catellectvl2bvisionlanguagemodeledgebased,
  title         = {Catellect-VL-2B: A Vision-Language Model for Edge-Based Feline Behavior Understanding},
  author        = {YuHang Wu and HaoXian Liu and Jia Tao},
  year          = {2026},
  eprint        = {2608.22070},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CE},
  url           = {https://arxiv.org/abs/2608.22070}
}

Qwen3-VL is licensed and attributed according to its original model card.