t.py

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 = "NewEden/Gemma-LN-merged",
    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 = False, # [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!
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    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,
)


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("NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed", 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 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 }
pass
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,
    args=SFTConfig(
        dataset_text_field="text",
        per_device_train_batch_size=1,
        gradient_accumulation_steps=4,
        warmup_steps=50,
        num_train_epochs=2,
        learning_rate=1e-5,
        max_grad_norm=0.2,
        logging_steps=1,
        optim="paged_adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="cosine",
        seed=3407,
        report_to="wandb",
        save_strategy="epoch",
    ),
)

"""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} %.")

"""<a name="Inference"></a>
### Inference
Let's run the model via Unsloth native inference! According to the `Gemma-3` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`
"""

from unsloth.chat_templates import get_chat_template
tokenizer = get_chat_template(
    tokenizer,
    chat_template = "gemma-3",
)
messages = [{
    "role": "user",
    "content": [{
        "type" : "text",
        "text" : "Continue the sequence: 1, 1, 2, 3, 5, 8,",
    }]
}]
text = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True, # Must add for generation
)
outputs = model.generate(
    **tokenizer([text], return_tensors = "pt").to("cuda"),
    max_new_tokens = 64, # Increase for longer outputs!
    # Recommended Gemma-3 settings!
    temperature = 1.0, top_p = 0.95, top_k = 64,
)
tokenizer.batch_decode(outputs)

""" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"""

messages = [{
    "role": "user",
    "content": [{"type" : "text", "text" : "Why is the sky blue?",}]
}]
text = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True, # Must add for generation
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer([text], return_tensors = "pt").to("cuda"),
    max_new_tokens = 64, # Increase for longer outputs!
    # Recommended Gemma-3 settings!
    temperature = 1.0, top_p = 0.95, top_k = 64,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

"""<a name="Save"></a>
### Saving, loading finetuned models
To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.

**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!
"""

model.save_pretrained("gemma-3")  # Local saving
tokenizer.save_pretrained("gemma-3")
# model.push_to_hub("HF_ACCOUNT/gemma-3", token = "...") # Online saving
# tokenizer.push_to_hub("HF_ACCOUNT/gemma-3", token = "...") # Online saving

"""Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"""

if False:
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
        max_seq_length = 2048,
        load_in_4bit = True,
    )

messages = [{
    "role": "user",
    "content": [{"type" : "text", "text" : "What is Gemma-3?",}]
}]
text = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True, # Must add for generation
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer([text], return_tensors = "pt").to("cuda"),
    max_new_tokens = 64, # Increase for longer outputs!
    # Recommended Gemma-3 settings!
    temperature = 1.0, top_p = 0.95, top_k = 64,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

"""### Saving to float16 for VLLM

We also support saving to `float16` directly for deployment! We save it in the folder `gemma-3-finetune`. Set `if False` to `if True` to let it run!
"""

if False: # Change to True to save finetune!
    model.save_pretrained_merged("gemma-3-finetune", tokenizer)

"""If you want to upload / push to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"""

if False: # Change to True to upload finetune
    model.push_to_hub_merged(
        "HF_ACCOUNT/gemma-3-finetune", tokenizer,
        token = "hf_..."
    )

"""### GGUF / llama.cpp Conversion
To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!
"""

if False: # Change to True to save to GGUF
    model.save_pretrained_gguf(
        "gemma-3-finetune",
        quantization_type = "Q8_0", # For now only Q8_0, BF16, F16 supported
    )

"""Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"""

if False: # Change to True to upload GGUF
    model.push_to_hub_gguf(
        "gemma-3-finetune",
        quantization_type = "Q8_0", # Only Q8_0, BF16, F16 supported
        repo_id = "HF_ACCOUNT/gemma-finetune-gguf",
        token = "hf_...",
    )

"""Now, use the `gemma-3-finetune.gguf` file or `gemma-3-finetune-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)

And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!

Some other links:
1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)
2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)
3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)
6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!

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  Join Discord if you need help + ⭐️ <i>Star us on <a href="https://github.com/unslothai/unsloth">Github</a> </i> ⭐️
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"""