Upload gemma.py

gemma.py

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+# -*- coding: utf-8 -*-
+"""Gemma3_(4B).ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B).ipynb
+
+To run this, press "*Runtime*" and press "*Run all*" on a **free** Tesla T4 Google Colab instance!
+<div class="align-center">
+<a href="https://unsloth.ai/"><img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="115"></a>
+<a href="https://discord.gg/unsloth"><img src="https://github.com/unslothai/unsloth/raw/main/images/Discord button.png" width="145"></a>
+<a href="https://docs.unsloth.ai/"><img src="https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true" width="125"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href="https://github.com/unslothai/unsloth">Github</a> </i> ⭐
+</div>
+
+To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).
+
+You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)
+
+### News
+
+**Read our [Gemma 3 blog](https://unsloth.ai/blog/gemma3) for what's new in Unsloth and our [Reasoning blog](https://unsloth.ai/blog/r1-reasoning) on how to train reasoning models.**
+
+Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).
+
+### Installation
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%capture
+# import os
+# if "COLAB_" not in "".join(os.environ.keys()):
+#     !pip install unsloth
+# else:
+#     # Do this only in Colab notebooks! Otherwise use pip install unsloth
+#     !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo
+#     !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer
+#     !pip install --no-deps unsloth
+# # Install latest Hugging Face for Gemma-3!
+# !pip install --no-deps git+https://github.com/huggingface/[email protected]
+
+"""### Unsloth
+
+`FastModel` supports loading nearly any model now! This includes Vision and Text models!
+"""
+
+from unsloth import FastModel
+import torch
+
+fourbit_models = [
+    # 4bit dynamic quants for superior accuracy and low memory use
+    "unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
+    "unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
+    "unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
+    "unsloth/gemma-3-27b-it-unsloth-bnb-4bit",
+
+    # Other popular models!
+    "unsloth/Llama-3.1-8B",
+    "unsloth/Llama-3.2-3B",
+    "unsloth/Llama-3.3-70B",
+    "unsloth/mistral-7b-instruct-v0.3",
+    "unsloth/Phi-4",
+] # More models at https://huggingface.co/unsloth
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name = "unsloth/gemma-3-4b-it",
+    max_seq_length = 8192, # Choose any for long context!
+    load_in_4bit = False,  # 4 bit quantization to reduce memory
+    load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory
+    full_finetuning = True, # [NEW!] We have full finetuning now!
+    # token = "hf_...", # use one if using gated models
+)
+
+"""We now add LoRA adapters so we only need to update a small amount of parameters!"""
+
+model = FastModel.get_peft_model(
+    model,
+    finetune_vision_layers     = False, # 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 = 64,           # Larger = higher accuracy, but might overfit
+    lora_alpha = 32,  # Recommended alpha == r at least
+    lora_dropout = 0.1,
+    bias = "none",
+    random_state = 3407,
+)
+
+"""<a name="Data"></a>
+### Data Prep
+We now use the `Gemma-3` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. Gemma-3 renders multi turn conversations like below:
+
+```
+<bos><start_of_turn>user
+Hello!<end_of_turn>
+<start_of_turn>model
+Hey there!<end_of_turn>
+```
+
+We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3, phi4, qwen2.5, gemma3` and more.
+"""
+
+from unsloth.chat_templates import get_chat_template
+tokenizer = get_chat_template(
+    tokenizer,
+    chat_template = "gemma-3",
+)
+from datasets import load_dataset
+dataset = load_dataset("FourOhFour/RP_Phase", split = "train")
+
+"""We now use `standardize_data_formats` to try converting datasets to the correct format for finetuning purposes!"""
+from unsloth.chat_templates import standardize_data_formats
+dataset = standardize_data_formats(dataset)
+
+"""Let's see how row 100 looks like!"""
+dataset[100]
+
+"""We validate and fix conversations to ensure proper role alternation"""
+def validate_and_fix_conversations(examples):
+    valid_convs = []
+    for conv in examples["conversations"]:
+        # Check if roles alternate properly
+        prev_role = None
+        
+        # Clean up the conversation to ensure proper alternation
+        fixed_conv = []
+        for turn in conv:
+            role = turn.get("role", "").lower()
+            # Skip if same role appears consecutively
+            if role == prev_role:
+                continue
+            # Normalize roles to expected format
+            if role in ["assistant", "bot", "chatbot"]:
+                role = "model"
+            elif role in ["human", "usr"]:
+                role = "user"
+            
+            fixed_conv.append({"role": role, "content": turn.get("content", "")})
+            prev_role = role
+            
+        # Ensure it starts with user and alternates correctly
+        if fixed_conv and fixed_conv[0]["role"] == "user":
+            valid_convs.append(fixed_conv)
+    
+    return {"conversations": valid_convs}
+
+# Apply the validation and fixing step
+dataset = dataset.map(validate_and_fix_conversations, batched=True)
+
+"""We now have to apply the chat template for `Gemma-3` onto the conversations, and save it to `text`"""
+def apply_chat_template(examples):
+    texts = tokenizer.apply_chat_template(examples["conversations"])
+    return { "text" : texts }
+
+dataset = dataset.map(apply_chat_template, batched = True)
+
+"""Let's see how the chat template did! Notice `Gemma-3` default adds a `<bos>`!"""
+dataset[100]["text"]
+
+"""<a name="Train"></a>
+### Train the model
+Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`.
+"""
+
+from trl import SFTTrainer, SFTConfig
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    eval_dataset = None, # Can set up evaluation!
+    args = SFTConfig(
+        dataset_text_field = "text",
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4, # Use GA to mimic batch size!
+        warmup_steps = 35,
+        num_train_epochs = 2, # Set this for 1 full training run.
+        learning_rate = 1e-5, # Reduce to 2e-5 for long training runs
+        logging_steps = 1,
+        optim = "paged_adamw_8bit",
+        weight_decay = 0.02,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        report_to = "wandb", # Use this for WandB etc
+    ),
+)
+
+"""We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes!"""
+
+from unsloth.chat_templates import train_on_responses_only
+trainer = train_on_responses_only(
+    trainer,
+    instruction_part = "<start_of_turn>user\n",
+    response_part = "<start_of_turn>model\n",
+)
+
+"""Let's verify masking the instruction part is done! Let's print the 100th row again:"""
+
+tokenizer.decode(trainer.train_dataset[100]["input_ids"])
+
+"""Now let's print the masked out example - you should see only the answer is present:"""
+
+tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100]["labels"]]).replace(tokenizer.pad_token, " ")
+
+# @title Show current memory stats
+gpu_stats = torch.cuda.get_device_properties(0)
+start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
+print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
+print(f"{start_gpu_memory} GB of memory reserved.")
+
+"""Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`"""
+
+trainer_stats = trainer.train()
+
+# @title Show final memory and time stats
+used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+used_memory_for_lora = round(used_memory - start_gpu_memory, 3)
+used_percentage = round(used_memory / max_memory * 100, 3)
+lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)
+print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
+print(
+    f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training."
+)
+print(f"Peak reserved memory = {used_memory} GB.")
+print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
+print(f"Peak reserved memory % of max memory = {used_percentage} %.")
+print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")