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Revert "Add Qwen2.5-VL-32B-Instruct mapping to fix quantized model merge erro…" (#2988)

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This reverts commit 73c02231de.
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Daniel Han 2025-07-17 15:35:28 -07:00 • committed by GitHub
commit 6631007493
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255
tests/saving/vision_models/test_save_merge_qwen2.5vl32B_model_ocr_benchmark.py
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@ -1,255 +0,0 @@
# -*- coding: utf-8 -*-
from unsloth import FastVisionModel
import torch
from qwen_vl_utils import process_vision_info
import os
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig
import sys
from pathlib import Path
REPO_ROOT = Path(__file__).parents[3]
sys.path.insert(0, str(REPO_ROOT))
from tests.utils.cleanup_utils import safe_remove_directory
from tests.utils.ocr_eval import OCRModelEvaluator
## Dataset Preparation
from datasets import load_dataset
dataset = load_dataset("lbourdois/OCR-liboaccn-OPUS-MIT-5M-clean", 'en', split="train")
# To select the first 2000 examples
train_dataset = dataset.select(range(2000))
# To select the next 200 examples for evaluation
eval_dataset = dataset.select(range(2000, 2200))
# Convert dataset to OAI messages
def format_data(sample):
return {"messages": [
{
"role": "system",
"content": [{"type": "text", "text": system_message}],
},
{
"role": "user",
"content": [
{
"type": "text",
"text": sample["question"],
},{
"type": "image",
"image": sample["image"],
}
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": sample["answer"]}],
},
],
}
system_message = "You are an expert french ocr system."
# Convert dataset to OAI messages
# need to use list comprehension to keep Pil.Image type, .mape convert image to bytes
train_dataset = [format_data(sample) for sample in train_dataset]
eval_dataset = [format_data(sample) for sample in eval_dataset]
## Setup OCR main evaluation function and helpers
import os
import torch
from tqdm import tqdm
import pandas as pd
from jiwer import wer, cer
from qwen_vl_utils import process_vision_info
#
ocr_evaluator = OCRModelEvaluator()
model_comparison_results = {}
## Finetuning Setup and Run
# Load Base Model
model, tokenizer = FastVisionModel.from_pretrained(
model_name = "unsloth/Qwen2.5-VL-32B-Instruct-bnb-4bit",
max_seq_length = 2048, # Choose any for long context!
load_in_4bit = True, # 4 bit quantization to reduce memory
load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory
full_finetuning = False, # [NEW!] We have full finetuning now!
)
# benchmark base model performance
model_name = "Unsloth Base model"
FastVisionModel.for_inference(model)
avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_base_model_results")
ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
## Lora Finetuning
model = FastVisionModel.get_peft_model(
model,
finetune_vision_layers = True, # Turn off for just text!
finetune_language_layers = True, # Should leave on!
finetune_attention_modules = True, # Attention good for GRPO
finetune_mlp_modules = True, # SHould leave on always!
r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
#target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
#"gate_proj", "up_proj", "down_proj",],
lora_alpha = 32,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
from unsloth import is_bf16_supported
from unsloth.trainer import UnslothVisionDataCollator
FastVisionModel.for_training(model) # Enable for training!
model.config.use_cache = False
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
data_collator = UnslothVisionDataCollator(model, tokenizer),
train_dataset = train_dataset,
args = SFTConfig(
#per_device_train_batch_size = 4,
#gradient_accumulation_steps = 8,
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
gradient_checkpointing=True,
gradient_checkpointing_kwargs = {"use_reentrant": False}, # use reentrant checkpointing
max_grad_norm=0.3, # max gradient norm based on QLoRA paper
warmup_ratio=0.03,
#num_train_epochs = 2, # Set this instead of max_steps for full training runs
max_steps=60,
learning_rate = 2e-4,
fp16 = not is_bf16_supported(),
bf16 = is_bf16_supported(),
logging_steps = 5,
save_strategy="epoch",
optim = "adamw_torch_fused",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "unsloth-qwen2.5-vl-32b-french-ocr-checkpoints",
report_to = "none", # For Weights and Biases
# You MUST put the below items for vision finetuning:
remove_unused_columns = False,
dataset_text_field = "",
dataset_kwargs = {"skip_prepare_dataset": True},
dataset_num_proc = 4,
max_seq_length = 2048,
),
)
# run training
trainer_stats = trainer.train()
model.save_pretrained("unsloth-qwen2.5-vl-32b-french-ocr-adapter", tokenizer)
tokenizer.save_pretrained("unsloth-qwen2.5-vl-32b-french-ocr-adapter")
## Measure Adapter Performance
# benchmark lora model performance
model_name = "Unsloth lora adapter model"
FastVisionModel.for_inference(model)
avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_lora_model_results")
ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
## Merge Model
def find_lora_base_model(model_to_inspect):
current = model_to_inspect
if hasattr(current, "base_model"):
current = current.base_model
if hasattr(current, "model"):
current = current.model
return current
pass
base = find_lora_base_model(model)
print((base.__class__.__name__))
# merge default 16 bits
model.save_pretrained_merged(save_directory="qwen2.5-ocr-merged-finetune-merge-16bit", tokenizer=tokenizer)
## Benchmark merged model performance
### 16 bits merged model
model, tokenizer = FastVisionModel.from_pretrained("./qwen2.5-ocr-merged-finetune-merge-16bit",load_in_4bit=False, load_in_8bit=False)
# benchmark 4bit loaded, 16bits merged model performance
model_name = "Unsloth 16bits-merged model load-16bits"
model.config.use_cache = True
avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_16bits_merged_model_load_16bits_results")
ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
# load 16bits-merged model in 4 bits
model, tokenizer = FastVisionModel.from_pretrained("./qwen2.5-ocr-merged-finetune-merge-16bit",load_in_4bit=True, load_in_8bit=False)
# benchmark 4bit loaded, 16bits merged model performance
model_name = "Unsloth 16bits-merged model load-4bits"
model.config.use_cache = True
avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_16bits_merged_model_load_4bits_results")
ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
# load model in 8 bits
model, tokenizer = FastVisionModel.from_pretrained("./qwen2.5-ocr-merged-finetune-merge-16bit",load_in_4bit=False, load_in_8bit=True)
# benchmark 4bit loaded, 16bits merged model performance
model_name = "Unsloth 16bits-merged model load-8bits"
avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_16bits_merged_model_load_8bits_results")
ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
# """### 4 bits merged model"""
#
# # load 4bits-merged model in 4 bits
# model, tokenizer = FastVisionModel.from_pretrained("./qwen2-ocr-merged-finetune-merge-4bit",load_in_4bit=True, load_in_8bit=False)
#
# # benchmark 4bit loaded, 4bits merged model performance
# model_name = "Unsloth 4bits-merged model load-4bits"
#
# avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_4bits_merged_model_load_4bits_results")
# ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
#
# # load model in 8 bits
# model, tokenizer = FastVisionModel.from_pretrained("./qwen2-ocr-merged-finetune-merge-4bit",load_in_4bit=False, load_in_8bit=True)
#
# # benchmark 8bit loaded, 4bits merged model performance
# model_name = "Unsloth 4bits-merged model load-8bits"
#
# avg_wer, avg_cer = ocr_evaluator.evaluate_model(model, tokenizer, eval_dataset, output_dir="unsloth_4bits_merged_model_load_8bits_results")
# ocr_evaluator.add_to_comparison(model_name, avg_wer, avg_cer)
# Model comparison report
#print model comparison
ocr_evaluator.print_model_comparison()
# Final cleanup
print("\n🧹 Cleaning up temporary files...")
safe_remove_directory("./unsloth-qwen2.5-vl-32b-french-ocr-adapter")
safe_remove_directory("./unsloth-qwen2.5-vl-32b-french-ocr-checkpoints")
safe_remove_directory("./unsloth_compiled_cache")
safe_remove_directory("./qwen2.5-ocr-merged-finetune-merge-16bit")
print("\n🎯 Pipeline completed successfully!")
print("=" * 80)

5
unsloth/models/mapper.py
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@ -618,11 +618,6 @@ __INT_TO_FLOAT_MAPPER = \
"Qwen/Qwen2.5-VL-7B-Instruct",
"unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit",
),
"unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit" : (
"unsloth/Qwen2.5-VL-32B-Instruct",
"Qwen/Qwen2.5-VL-32B-Instruct",
"unsloth/Qwen2.5-VL-32B-Instruct-bnb-4bit",
),
"unsloth/Qwen2.5-VL-72B-Instruct-unsloth-bnb-4bit" : (
"unsloth/Qwen2.5-VL-72B-Instruct",
"Qwen/Qwen2.5-VL-72B-Instruct",