You can deploy this model and start earning money today!

DSA1/
qwen25vl-7b-perdataset-derivatives

Name: qwen25vl-7b-perdataset-derivatives
Availability: InStock

Qwen/Qwen2.5-VL-7B-Instruct

adapter

Views

No views yet

Qwen2.5-VL-7B — Per-Dataset Fine-Tuning Derivatives (LoRA + Full FT)

10 fine-tuned derivatives of Qwen/Qwen2.5-VL-7B-Instruct, one LoRA and one full fine-tune for each of 5 vision-language datasets. Produced as clean single-dataset transfer targets for a visual-fingerprint transfer study.

⚠️ Research artifacts. Each model is trained on a 3,000-example slice of one dataset for 3 epochs — these are experimental derivatives, not production models.

Shared training recipe (all 10)

Base: Qwen/Qwen2.5-VL-7B-Instruct
Frozen: vision tower + multimodal projector (freeze_vision_tower: true, freeze_multi_modal_projector: true) — only the language-model backbone is trained.
Data: max_samples = 3000 per dataset, 3 epochs, cutoff_len = 2048.
Framework: LLaMA-Factory 0.9.3, transformers 4.52.4.
LoRA variant: target q_proj, v_proj; rank 16, alpha 32; lr 2e-4; trainable ≈ 0.06 % of params.
Full variant: DeepSpeed ZeRO-3; lr 1e-5; image_max_pixels = 401408; trainable = 100 % of the LM (vision/projector still frozen).

Path	Type	Trainable	train_loss (3 ep)
`lora/mathv360k`	LoRA (q/v r16)	~0.06 %	0.151
`lora/paintingform`	LoRA (q/v r16)	~0.06 %	0.732
`lora/stvqa`	LoRA (q/v r16)	~0.06 %	0.351
`lora/textvqa`	LoRA (q/v r16)	~0.06 %	0.177
`lora/visual7w`	LoRA (q/v r16)	~0.06 %	0.527
`full/mathv360k`	Full FT (ZeRO-3)	100 % LM	0.148
`full/paintingform`	Full FT (ZeRO-3)	100 % LM	0.543
`full/stvqa`	Full FT (ZeRO-3)	100 % LM	0.262
`full/textvqa`	Full FT (ZeRO-3)	100 % LM	0.211
`full/visual7w`	Full FT (ZeRO-3)	100 % LM	0.398

Datasets: MathV360K, PaintingForm, ST-VQA, TextVQA, Visual7W (single-dataset VQA-style instruction tuning).

Usage

LoRA (needs the base model + PEFT):

python

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from peft import PeftModel
base = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "DSA1/qwen25vl-7b-perdataset-derivatives", subfolder="lora/textvqa")
proc = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

Full FT (self-contained weights):

python

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained("DSA1/qwen25vl-7b-perdataset-derivatives", subfolder="full/textvqa", torch_dtype="auto", device_map="auto")
proc = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")   # processor/chat-template from base

Notes

train_loss is the mean training loss over all optimizer steps (the LoRA runs ~300 steps for the 4 smaller datasets and ~1014 for textvqa; the full runs are 129 steps at global batch 64).
full/textvqa was taken from the run's checkpoint-129 (the final training step): its top-level save was interrupted by a cluster preemption, but checkpoint-129 holds the complete 4-shard, 16.6 GB, 729-tensor model. It is the fully-trained end-of-run model.
Full-FT models ship the LM weights only; load the processor / chat template from the base Qwen/Qwen2.5-VL-7B-Instruct.

License

Inherits the base model's license (Qwen/Qwen2.5-VL-7B-Instruct). Datasets retain their respective licenses.

DSA1/qwen25vl-7b-perdataset-derivatives