Merge pull request #383 from MervinPraison/develop

Enhance Vision Model Training with Robust Dataset Conversion and Memo…

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.72 gunicorn markdown
+RUN pip install flask praisonai==2.0.76 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.72 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.76 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.72.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.76.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/cli.py

@@ -240,7 +240,18 @@ class PraisonAI:
                 print("All packages installed.")
 
             train_args = sys.argv[2:]  # Get all arguments after 'train'
-            train_script_path = os.path.join(package_root, 'train.py')
+            
+            # Check if this is a vision model - handle all case variations
+            model_name = config.get("model_name", "").lower()
+            is_vision_model = any(x in model_name for x in ["-vl-", "-vl", "vl-", "-vision-", "-vision", "vision-", "visionmodel"])
+            
+            # Choose appropriate training script
+            if is_vision_model:
+                train_script_path = os.path.join(package_root, 'train_vision.py')
+                print("Using vision training script for VL model...")
+            else:
+                train_script_path = os.path.join(package_root, 'train.py')
+                print("Using standard training script...")
 
             # Set environment variables
             env = os.environ.copy()

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.72 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.76 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

praisonai/setup/setup_conda_env.sh

@@ -96,7 +96,7 @@ PIP_FULL_PATH=$(conda run -n "$ENV_NAME" which pip)
 # Install other packages using pip
 $PIP_FULL_PATH install --upgrade pip 
 $PIP_FULL_PATH install "xformers==0.0.26.post1"
-$PIP_FULL_PATH install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@038e6d4c8d40207a87297ab3aaf787c19b1006d1"
+$PIP_FULL_PATH install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@53a773e4fbc53a1d96c7ba107e5fe75dab07027b"
 $PIP_FULL_PATH install --no-deps "trl<0.9.0" peft accelerate bitsandbytes 
 $PIP_FULL_PATH install unsloth_zoo
 $PIP_FULL_PATH install cut_cross_entropy

praisonai/train.py

@@ -216,25 +216,29 @@ class TrainModel:
         raw_dataset = self.load_datasets()
         tokenized_dataset = self.tokenize_dataset(raw_dataset)
         print("DEBUG: Dataset tokenization complete.")
-        training_args = TrainingArguments(
-            per_device_train_batch_size=2,
-            gradient_accumulation_steps=4,
-            warmup_steps=5,
-            max_steps=60,
-            learning_rate=2e-4,
-            fp16=not is_bfloat16_supported(),
-            bf16=is_bfloat16_supported(),
-            logging_steps=1,
-            optim="adamw_8bit",
-            weight_decay=0.01,
-            lr_scheduler_type="linear",
-            seed=3407,
-            output_dir="outputs",
-            report_to="none" if not os.getenv("PRAISON_WANDB") else "wandb",
-            save_steps=100 if os.getenv("PRAISON_WANDB") else None,
-            run_name=os.getenv("PRAISON_WANDB_RUN_NAME", "praisonai-train") if os.getenv("PRAISON_WANDB") else None,
-            remove_unused_columns=False,
-        )
+        # Build the training arguments parameters dynamically
+        ta_params = {
+            "per_device_train_batch_size": self.config.get("per_device_train_batch_size", 2),
+            "gradient_accumulation_steps": self.config.get("gradient_accumulation_steps", 2),
+            "warmup_steps": self.config.get("warmup_steps", 50),
+            "max_steps": self.config.get("max_steps", 2800),
+            "learning_rate": self.config.get("learning_rate", 2e-4),
+            "fp16": self.config.get("fp16", not is_bfloat16_supported()),
+            "bf16": self.config.get("bf16", is_bfloat16_supported()),
+            "logging_steps": self.config.get("logging_steps", 15),
+            "optim": self.config.get("optim", "adamw_8bit"),
+            "weight_decay": self.config.get("weight_decay", 0.01),
+            "lr_scheduler_type": self.config.get("lr_scheduler_type", "linear"),
+            "seed": self.config.get("seed", 3407),
+            "output_dir": self.config.get("output_dir", "outputs"),
+            "report_to": "none" if not os.getenv("PRAISON_WANDB") else "wandb",
+            "remove_unused_columns": self.config.get("remove_unused_columns", False)
+        }
+        if os.getenv("PRAISON_WANDB"):
+            ta_params["save_steps"] = self.config.get("save_steps", 100)
+            ta_params["run_name"] = os.getenv("PRAISON_WANDB_RUN_NAME", "praisonai-train")
+
+        training_args = TrainingArguments(**ta_params)
         # Since the dataset is pre-tokenized, we supply a dummy dataset_text_field.
         trainer = SFTTrainer(
             model=self.model,
@@ -425,12 +429,16 @@ class TrainModel:
                 "template": """{{- if .System }}{{ .System }}{{ end }}
     {{- range $i, $_ := .Messages }}
     {{- $last := eq (len (slice $.Messages $i)) 1}}
-    {{- if eq .Role "user" }}<｜User｜>{{ .Content }}
-    {{- else if eq .Role "assistant" }}<｜Assistant｜>{{ .Content }}{{- if not $last }}<｜end▁of▁sentence｜>{{- end }}
+    {{- if eq .Role "user" }}
+    {{ .Content }}
+    {{- else if eq .Role "assistant" }}
+    {{ .Content }}{{- if not $last }}
     {{- end }}
-    {{- if and $last (ne .Role "assistant") }}<｜Assistant｜>{{- end }}
+    {{- end }}
+    {{- if and $last (ne .Role "assistant") }}
+    {{ end }}
     {{- end }}""",
-                "stop_tokens": ["<｜begin▁of▁sentence｜>", "<｜end▁of▁sentence｜>", "<｜User｜>", "<｜Assistant｜>"]
+                "stop_tokens": ["", "", "", ""]
             },
             "llava": {
                 "template": """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>

praisonai/train_vision.py

@@ -0,0 +1,274 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This script finetunes a vision language model using Unsloth's fast training framework.
+It supports vision tasks by converting raw image-caption samples into a conversation format, 
+adding vision-specific LoRA adapters, and training using TRL's SFTTrainer with UnslothVisionDataCollator.
+"""
+
+import os
+import sys
+import yaml
+import torch
+import shutil
+import subprocess
+import gc  # For garbage collection
+
+from datasets import load_dataset, concatenate_datasets, Dataset
+from unsloth import FastVisionModel, is_bf16_supported
+from unsloth.trainer import UnslothVisionDataCollator
+from transformers import TrainingArguments
+from trl import SFTTrainer
+from tqdm import tqdm  # Add progress bar
+
+
+class TrainVisionModel:
+    def __init__(self, config_path="config.yaml"):
+        self.load_config(config_path)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = None
+        self.hf_tokenizer = None  # The underlying tokenizer
+
+    def load_config(self, path):
+        with open(path, "r") as file:
+            self.config = yaml.safe_load(file)
+        print("DEBUG: Loaded config:", self.config)
+
+    def print_system_info(self):
+        print("DEBUG: PyTorch version:", torch.__version__)
+        print("DEBUG: CUDA version:", torch.version.cuda)
+        if torch.cuda.is_available():
+            print("DEBUG: CUDA Device Capability:", torch.cuda.get_device_capability())
+        else:
+            print("DEBUG: CUDA is not available")
+        print("DEBUG: Python Version:", sys.version)
+        print("DEBUG: Python Path:", sys.executable)
+
+    def check_gpu(self):
+        gpu_stats = torch.cuda.get_device_properties(0)
+        print(f"DEBUG: GPU = {gpu_stats.name}. Max memory = {round(gpu_stats.total_memory/(1024**3),3)} GB.")
+
+    def check_ram(self):
+        from psutil import virtual_memory
+        ram_gb = virtual_memory().total / 1e9
+        print(f"DEBUG: Your runtime has {ram_gb:.1f} gigabytes of available RAM")
+        if ram_gb < 20:
+            print("DEBUG: Not using a high-RAM runtime")
+        else:
+            print("DEBUG: You are using a high-RAM runtime!")
+
+    def prepare_model(self):
+        print("DEBUG: Preparing vision model and tokenizer...")
+        self.model, original_tokenizer = FastVisionModel.from_pretrained(
+            model_name=self.config["model_name"],
+            load_in_4bit=self.config["load_in_4bit"],
+            use_gradient_checkpointing="unsloth"
+        )
+        print("DEBUG: Vision model and original tokenizer loaded.")
+        
+        # Use the full processor that supports image inputs.
+        self.hf_tokenizer = original_tokenizer
+        
+        # Set pad token if needed
+        if not hasattr(self.hf_tokenizer, 'pad_token') or self.hf_tokenizer.pad_token is None:
+            if hasattr(self.hf_tokenizer, 'eos_token'):
+                self.hf_tokenizer.pad_token = self.hf_tokenizer.eos_token
+            elif hasattr(self.hf_tokenizer, 'bos_token'):
+                self.hf_tokenizer.pad_token = self.hf_tokenizer.bos_token
+        
+        # Set max length
+        if hasattr(self.hf_tokenizer, 'model_max_length'):
+            self.hf_tokenizer.model_max_length = self.config.get("max_seq_length", 2048)
+        
+        # Add vision-specific LoRA adapters
+        self.model = FastVisionModel.get_peft_model(
+            self.model,
+            finetune_vision_layers=self.config.get("finetune_vision_layers", False),
+            finetune_language_layers=self.config.get("finetune_language_layers", True),
+            finetune_attention_modules=self.config.get("finetune_attention_modules", True),
+            finetune_mlp_modules=self.config.get("finetune_mlp_modules", True),
+            r=16,
+            lora_alpha=16,
+            lora_dropout=0,
+            bias="none",
+            random_state=3407,
+            use_rslora=False,
+            loftq_config=None
+        )
+        print("DEBUG: Vision LoRA adapters added.")
+
+    def convert_sample(self, sample):
+        
+        instruction = self.config.get(
+            "vision_instruction",
+            "You are an expert radiographer. Describe accurately what you see in this image."
+        )
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": instruction},
+                    {"type": "image", "image": sample["image"]}
+                ]
+            },
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": sample["caption"]}
+                ]
+            },
+        ]
+        
+        return {"messages": conversation}
+
+    def load_datasets(self):
+        all_converted = []
+        for dataset_info in self.config["dataset"]:
+            print("\nDEBUG: Loading vision dataset:", dataset_info)
+            ds = load_dataset(
+                dataset_info["name"], 
+                split=dataset_info.get("split_type", "train")
+            )
+            print("DEBUG: Dataset size:", len(ds))
+            print("DEBUG: First raw sample:", ds[0])
+            print("DEBUG: Dataset features:", ds.features)
+            
+            print("\nDEBUG: Converting dataset to vision conversation format...")
+            converted_ds = [self.convert_sample(sample) for sample in ds]
+            
+            # Debug first converted sample
+            print("\nDEBUG: First converted sample structure:")
+            first = converted_ds[0]
+            print("DEBUG: Message keys:", first["messages"][0]["content"][1].keys())
+            print("DEBUG: Image type in converted:", type(first["messages"][0]["content"][1].get("image")))
+            
+            all_converted.extend(converted_ds)
+        
+        print("\nDEBUG: Combined vision dataset has", len(all_converted), "examples.")
+        return all_converted
+
+    def train_model(self):
+        print("DEBUG: Starting vision training...")
+        raw_dataset = self.load_datasets()
+        
+        # Build training arguments using TrainingArguments
+        training_args = TrainingArguments(
+            per_device_train_batch_size=self.config.get("per_device_train_batch_size", 1),
+            gradient_accumulation_steps=self.config.get("gradient_accumulation_steps", 4),
+            warmup_steps=self.config.get("warmup_steps", 5),
+            max_steps=self.config.get("max_steps", 30),
+            learning_rate=self.config.get("learning_rate", 2e-4),
+            fp16=self.config.get("fp16", not is_bf16_supported()),
+            bf16=self.config.get("bf16", is_bf16_supported()),
+            logging_steps=self.config.get("logging_steps", 1),
+            optim=self.config.get("optim", "adamw_8bit"),
+            weight_decay=self.config.get("weight_decay", 0.01),
+            lr_scheduler_type=self.config.get("lr_scheduler_type", "linear"),
+            seed=self.config.get("seed", 3407),
+            output_dir=self.config.get("output_dir", "outputs"),
+            report_to="none" if not os.getenv("PRAISON_WANDB") else "wandb",
+            remove_unused_columns=False,
+            # Add memory optimization settings
+            gradient_checkpointing=True,
+            max_grad_norm=1.0,
+        )
+        
+        trainer = SFTTrainer(
+            model=self.model,
+            tokenizer=self.hf_tokenizer,
+            data_collator=UnslothVisionDataCollator(self.model, self.hf_tokenizer),
+            train_dataset=raw_dataset,
+            args=training_args,
+            max_seq_length=self.config.get("max_seq_length", 2048),
+            dataset_text_field="",  # Required for vision training
+            dataset_kwargs={"skip_prepare_dataset": True},  # Required for vision training
+            packing=False  # Explicitly set packing to False
+        )
+        print("DEBUG: Beginning vision trainer.train() ...")
+        trainer.train()
+        print("DEBUG: Vision training complete. Saving model and tokenizer locally...")
+        self.model.save_pretrained("lora_vision_model")
+        self.hf_tokenizer.save_pretrained("lora_vision_model")
+        print("DEBUG: Saved vision model and tokenizer to 'lora_vision_model'.")
+
+    def vision_inference(self, instruction, image):
+        FastVisionModel.for_inference(self.model)
+        messages = [
+            {"role": "user", "content": [
+                {"type": "image"},
+                {"type": "text", "text": instruction}
+            ]}
+        ]
+        input_text = self.hf_tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = self.hf_tokenizer(
+            image,
+            input_text,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to("cuda")
+        outputs = self.model.generate(
+            **inputs,
+            max_new_tokens=128,
+            use_cache=True,
+            temperature=1.5,
+            min_p=0.1
+        )
+        print("DEBUG: Vision inference output:", self.hf_tokenizer.batch_decode(outputs))
+
+    def save_model_merged(self):
+        if os.path.exists(self.config["hf_model_name"]):
+            shutil.rmtree(self.config["hf_model_name"])
+        self.model.push_to_hub_merged(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            save_method="merged_16bit",
+            token=os.getenv("HF_TOKEN")
+        )
+
+    def push_model_gguf(self):
+        self.model.push_to_hub_gguf(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            quantization_method=self.config.get("quantization_method", "q4_k_m"),
+            token=os.getenv("HF_TOKEN")
+        )
+
+    def save_model_gguf(self):
+        self.model.save_pretrained_gguf(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            quantization_method="q4_k_m"
+        )
+
+    def run(self):
+        self.print_system_info()
+        self.check_gpu()
+        self.check_ram()
+        if self.config.get("train", "true").lower() == "true":
+            self.prepare_model()
+            self.train_model()
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="PraisonAI Vision Training Script")
+    parser.add_argument("command", choices=["train", "inference"], help="Command to execute")
+    parser.add_argument("--config", default="config.yaml", help="Path to configuration file")
+    args = parser.parse_args()
+
+    trainer_obj = TrainVisionModel(config_path=args.config)
+    if args.command == "train":
+        trainer_obj.run()
+    elif args.command == "inference":
+        # For inference, we load a sample image from the first dataset
+        instr = trainer_obj.config.get("vision_instruction", "You are an expert radiographer. Describe accurately what you see in this image.")
+        ds_info = trainer_obj.config["dataset"][0]
+        ds = load_dataset(ds_info["name"], split=ds_info.get("split_type", "train"))
+        sample_image = ds[0]["image"]
+        if trainer_obj.model is None or trainer_obj.hf_tokenizer is None:
+            trainer_obj.prepare_model()
+        trainer_obj.vision_inference(instr, sample_image)
+
+
+if __name__ == "__main__":
+    main() 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.72"
+version = "2.0.76"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.72"
+version = "2.0.76"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
 authors = ["Mervin Praison"]
 license = ""

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.72"
+version = "2.0.76"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },