A Step-by-Step Coding Information to Effectively Tremendous-Tune Qwen3-14B Utilizing Unsloth AI on Google Colab with Combined Datasets and LoRA Optimization

Tremendous-tuning LLMs typically requires intensive sources, time, and reminiscence, challenges that may hinder speedy experimentation and deployment. Unsloth AI revolutionizes this course of by enabling quick, environment friendly fine-tuning state-of-the-art fashions like Qwen3-14B with minimal GPU reminiscence, leveraging superior strategies comparable to 4-bit quantization and LoRA (Low-Rank Adaptation). On this tutorial, we stroll by a sensible implementation on Google Colab to fine-tune Qwen3-14B utilizing a mixture of reasoning and instruction-following datasets, combining Unsloth’s FastLanguageModel utilities with trl.SFTTrainer customers can obtain highly effective fine-tuning efficiency with simply consumer-grade {hardware}.

%%seize
import os
if "COLAB_" not in "".be part of(os.environ.keys()):
    !pip set up unsloth
else:
    !pip set up --no-deps bitsandbytes speed up xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo
    !pip set up sentencepiece protobuf "datasets>=3.4.1" huggingface_hub hf_transfer
    !pip set up --no-deps unsloth

We set up all of the important libraries required for fine-tuning the Qwen3 mannequin utilizing Unsloth AI. It conditionally installs dependencies primarily based on the atmosphere, utilizing a light-weight strategy on Colab to make sure compatibility and scale back overhead. Key parts like bitsandbytes, trl, xformers, and unsloth_zoo are included to allow 4-bit quantized coaching and LoRA-based optimization.

from unsloth import FastLanguageModel
import torch


mannequin, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Qwen3-14B",
    max_seq_length = 2048,
    load_in_4bit = True,
    load_in_8bit = False,
    full_finetuning = False,
)

We load the Qwen3-14B mannequin utilizing FastLanguageModel from the Unsloth library, which is optimized for environment friendly fine-tuning. It initializes the mannequin with a context size of 2048 tokens and masses it in 4-bit precision, considerably lowering reminiscence utilization. Full fine-tuning is disabled, making it appropriate for light-weight parameter-efficient strategies like LoRA.

mannequin = FastLanguageModel.get_peft_model(
    mannequin,
    r = 32,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj"],
    lora_alpha = 32,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = False,
    loftq_config = None,
)

We apply LoRA (Low-Rank Adaptation) to the Qwen3 mannequin utilizing FastLanguageModel.get_peft_model. It injects trainable adapters into particular transformer layers (like q_proj, v_proj, and so on.) with a rank of 32, enabling environment friendly fine-tuning whereas conserving most mannequin weights frozen. Utilizing “unsloth” gradient checkpointing additional optimizes reminiscence utilization, making it appropriate for coaching giant fashions on restricted {hardware}.

from datasets import load_dataset


reasoning_dataset = load_dataset("unsloth/OpenMathReasoning-mini", break up="cot")
non_reasoning_dataset = load_dataset("mlabonne/FineTome-100k", break up="prepare")

We load two pre-curated datasets from the Hugging Face Hub utilizing the library. The reasoning_dataset incorporates chain-of-thought (CoT) issues from Unsloth’s OpenMathReasoning-mini, designed to boost logical reasoning within the mannequin. The non_reasoning_dataset pulls common instruction-following information from mlabonne’s FineTome-100k, which helps the mannequin be taught broader conversational and task-oriented expertise. Collectively, these datasets assist a well-rounded fine-tuning goal.

def generate_conversation(examples):
    issues  = examples["problem"]
    options = examples["generated_solution"]
    conversations = []
    for drawback, resolution in zip(issues, options):
        conversations.append([
            {"role": "user", "content": problem},
            {"role": "assistant", "content": solution},
        ])
    return {"conversations": conversations}

This operate, generate_conversation, transforms uncooked query–reply pairs from the reasoning dataset right into a chat-style format appropriate for fine-tuning. For every drawback and its corresponding generated resolution, a dialog is performed during which the consumer asks a query and the assistant gives the reply. The output is a listing of dictionaries following the construction anticipated by chat-based language fashions, making ready the info for tokenization with a chat template.

reasoning_conversations = tokenizer.apply_chat_template(
    reasoning_dataset["conversations"],
    tokenize=False,
)


from unsloth.chat_templates import standardize_sharegpt
dataset = standardize_sharegpt(non_reasoning_dataset)


non_reasoning_conversations = tokenizer.apply_chat_template(
    dataset["conversations"],
    tokenize=False,
)


import pandas as pd


chat_percentage = 0.75
non_reasoning_subset = pd.Collection(non_reasoning_conversations).pattern(
    int(len(reasoning_conversations) * (1.0 - chat_percentage)),
    random_state=2407,
)


information = pd.concat([
    pd.Series(reasoning_conversations),
    pd.Series(non_reasoning_subset)
])
information.title = "textual content"

We put together the fine-tuning dataset by changing the reasoning and instruction datasets right into a constant chat format after which combining them. It first applies the tokenizer’s apply_chat_template to transform structured conversations into tokenizable strings. The standardize_sharegpt operate normalizes the instruction dataset right into a appropriate construction. Then, a 75-25 combine is created by sampling 25% of the non-reasoning (instruction) conversations and mixing them with the reasoning information. This mix ensures the mannequin is uncovered to logical reasoning and common instruction-following duties, bettering its versatility throughout coaching. The ultimate mixed information is saved as a single-column Pandas Collection named “textual content”.

from datasets import Dataset


combined_dataset = Dataset.from_pandas(pd.DataFrame(information))
combined_dataset = combined_dataset.shuffle(seed=3407)


from trl import SFTTrainer, SFTConfig


coach = SFTTrainer(
    mannequin=mannequin,
    tokenizer=tokenizer,
    train_dataset=combined_dataset,
    eval_dataset=None,  
    args=SFTConfig(
        dataset_text_field="textual content",
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=5,
        max_steps=30,
        learning_rate=2e-4,
        logging_steps=1,
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        seed=3407,
        report_to="none",
    )
)

We take the preprocessed conversations, wrap them right into a Hugging Face Dataset (making certain the info is in a constant format), and shuffle the dataset with a hard and fast seed for reproducibility. Then, the fine-tuning coach is initialized utilizing trl’s SFTTrainer and SFTConfig. The coach is ready up to make use of the mixed dataset (with the textual content column area named “textual content”) and defines coaching hyperparameters like batch dimension, gradient accumulation, variety of warmup and coaching steps, studying fee, optimizer parameters, and a linear studying fee scheduler. This configuration is geared in direction of environment friendly fine-tuning whereas sustaining reproducibility and logging minimal particulars (with report_to=”none”).

coach.prepare() begins the fine-tuning course of for the Qwen3-14B mannequin utilizing the SFTTrainer. It trains the mannequin on the ready blended dataset of reasoning and instruction-following conversations, optimizing solely the LoRA-adapted parameters because of the underlying Unsloth setup. Coaching will proceed in keeping with the configuration specified earlier (e.g., max_steps=30, batch_size=2, lr=2e-4), and progress shall be printed each logging step. This last command launches the precise mannequin adaptation primarily based in your customized information.

mannequin.save_pretrained("qwen3-finetuned-colab")
tokenizer.save_pretrained("qwen3-finetuned-colab")

We save the fine-tuned mannequin and tokenizer regionally to the “qwen3-finetuned-colab” listing. By calling save_pretrained(), the tailored weights and tokenizer configuration might be reloaded later for inference or additional coaching, regionally or for importing to the Hugging Face Hub.

In conclusion, with the assistance of Unsloth AI, fine-tuning large LLMs like Qwen3-14B turns into possible, utilizing restricted sources, and is extremely environment friendly and accessible. This tutorial demonstrated how you can load a 4-bit quantized model of the mannequin, apply structured chat templates, combine a number of datasets for higher generalization, and prepare utilizing TRL’s SFTTrainer. Whether or not you’re constructing customized assistants or specialised area fashions, Unsloth’s instruments dramatically scale back the barrier to fine-tuning at scale. As open-source fine-tuning ecosystems evolve, Unsloth continues to cleared the path in making LLM coaching quicker, cheaper, and extra sensible for everybody.

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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.