Revert "Revert "Add Qwen2.5-VL-32B-Instruct mapping to fix quantized …

tests/saving/vision_models/test_save_merge_qwen2.5vl32B_model_ocr_benchmark.py

@@ -0,0 +1,255 @@
+# -*- 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)

unsloth/models/mapper.py

@@ -618,6 +618,11 @@
         "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",