{"id":127816,"date":"2025-03-12T05:57:25","date_gmt":"2025-03-12T05:57:25","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/refining-english-to-hinglish-translations-with-gemma-2-9b\/"},"modified":"2025-03-12T05:57:25","modified_gmt":"2025-03-12T05:57:25","slug":"refining-english-to-hinglish-translations-with-gemma-2-9b","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=127816","title":{"rendered":"Refining English-to-Hinglish Translations with Gemma 2 9B"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>Have you ever thought about how to make communication easier for people who use a mix of Hindi and English, commonly known as Hinglish? With the growing use of Hinglish in everyday conversations, social media, and advertising, there\u2019s a need for tools that can accurately translate between English and Hinglish. This is where advanced language models like\u00a0Gemma 2 9B\u00a0come into play. By fine-tuning this model, we can create solutions that understand the unique blend of Hindi and English, making communication more effective for a wider audience.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-learning-objectives\">Learning Objectives<\/h3>\n<ul class=\"wp-block-list\">\n<li>Understand the key features and multilingual capabilities of the Gemma 2 9B model.<\/li>\n<li>Learn how Unsloth AI accelerates fine-tuning for large language models.<\/li>\n<li>Gain hands-on experience in fine-tuning the Gemma 2 9B model for English-to-Hinglish translation.<\/li>\n<li>Explore the impact of fine-tuning on translation accuracy compared to the original model.<\/li>\n<li>Learn how to deploy and query the fine-tuned model <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2024\/07\/local-llm-deployment-with-ollama\/\" target=\"_blank\" rel=\"noreferrer noopener\">using Ollama<\/a> for real-world applications.<\/li>\n<\/ul>\n<p><em><strong>This article was published as a part of the\u00a0<\/strong><\/em><a href=\"https:\/\/www.analyticsvidhya.com\/datahack\/blogathon\" target=\"_blank\" rel=\"noreferrer noopener\"><em><strong>Data Science Blogathon.<\/strong><\/em><\/a><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-understanding-gemma-2-9b-model\">Understanding Gemma 2 9B Model<\/h2>\n<p>Gemma 2 models represent a significant advancement in<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/09\/introduction-to-artificial-intelligence-for-beginners\/\" target=\"_blank\" rel=\"noreferrer noopener\"> artificial intelligence<\/a>, offering powerful language processing capabilities with a focus on efficiency and accessibility. These models are designed to excel in tasks such as text generation, code writing, and problem-solving. With their compact size and robust performance, Gemma 2 models provide a versatile tool for developers and users alike. They are particularly noted for their competitive performance relative to larger models.<\/p>\n<ul class=\"wp-block-list\">\n<li><b>Parameter Size:<\/b> The model has<b> 9 billion parameters<\/b>, which is relatively small compared to other larger LLMs, making it efficient for deployment on devices with limited resources<\/li>\n<li><b>Training Data:<\/b> It was trained on a<b> massive dataset of 8 trillion tokens<\/b>, including web documents, code, and mathematical text. This diverse training enables the model to excel in tasks like text generation, code writing, and mathematical problem-solving<\/li>\n<li><b>Architecture: <\/b>Gemma 2 uses a transformer architecture, which is well-suited for natural language processing tasks. It is designed to handle a wide range of tasks, from answering questions to generating code<\/li>\n<li><b>Multilingual and Code Generation: <\/b>Gemma 2 is proficient in multiple languages and can generate code in various programming languages, making it a versatile tool for developers<\/li>\n<li><b>Efficiency and Accessibility:<\/b> Its relatively small size allows for deployment on laptops or desktops, democratizing access to state-of-the-art AI models. It also supports fast inference, making it suitable for real-time applications<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-fine-tuning-gemma-2-9b-using-unsloth-ai\">Fine tuning Gemma 2 9B using Unsloth AI<\/h2>\n<p>Fine-tuning the multilingual Gemma 2 9B model can be highly beneficial for Hindi translations due to its robust multilingual capabilities and adaptability.<\/p>\n<ul class=\"wp-block-list\">\n<li><b>Multilingual Strengths:<\/b> Gemma 2 models, including the 9B version, have demonstrated strong multilingual performance across various languages, often surpassing larger models like Llama-3-70B in specific tasks. For instance, fine-tuned versions have excelled in languages such as French and Korean, showcasing their ability to handle diverse linguistic structures effectively. This capability indicates that with fine-tuning on Hinglish datasets, the model can achieve high-quality translations and semantic understanding.<\/li>\n<li><b>Customization for Hindi: <\/b>Fine-tuning allows the model to adapt specifically to Hinglish unique syntax, grammar, and cultural nuances. Using techniques like Supervised Fine-Tuning (SFT) or Low-Rank Adaptation (LoRA), developers can enhance its translation accuracy by training it on curated Hinglish datasets. This process ensures that the model generates contextually accurate and culturally relevant translations.<\/li>\n<li><b>Efficiency for Low-Resource Scenarios: <\/b>The Gemma 2 9B model is computationally efficient compared to larger models like the 27B version, making it ideal for projects with limited resources while still delivering excellent result<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-unsloth-ai\">What is Unsloth AI?<\/h2>\n<p>Unsloth AI, founded in 2023 and based in San Francisco, is an innovative startup revolutionizing the fine-tuning and training of large language models (LLMs). With a focus on speed and efficiency, Unsloth\u2019s platform enables model training up to 30 times faster while using 90% less memory compared to traditional methods. This is achieved through advanced software optimizations, such as handwritten GPU kernels, rather than relying on hardware upgrades. The company embraces an open-source approach, boasting over 8 million monthly downloads and 29,000 GitHub stars. By making AI training more accessible and cost-effective, Unsloth AI caters to developers and enterprises alike, fostering a collaborative and inclusive AI ecosystem.<\/p>\n<p>Unsloth speeds up LLM training using several techniques. It manually derives backpropagation steps, like manual autograd, for faster gradient calculations. And optimizes chained matrix multiplications and builds custom, more efficient kernels known as Triton language kernels. It also uses Flash Attention to focus on critical input data. Along with other memory-efficient strategies, these enhance training speed and efficiency.<\/p>\n<figure class=\"wp-block-image size-full figure mt-2 mb-2 d-table mx-auto\"><img fetchpriority=\"high\" decoding=\"async\" width=\"820\" height=\"208\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/How-Unsloth-Enables-Faster-Fine-Tuning.webp\" alt=\"How Unsloth Enables Faster Fine Tuning; translations with Gemma 2 9B\" class=\"wp-image-225686\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/How-Unsloth-Enables-Faster-Fine-Tuning.webp 820w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/How-Unsloth-Enables-Faster-Fine-Tuning-300x76.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/How-Unsloth-Enables-Faster-Fine-Tuning-768x195.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/How-Unsloth-Enables-Faster-Fine-Tuning-150x38.webp 150w\" sizes=\"(max-width: 820px) 100vw, 820px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-hands-on-tutorial-on-fine-tuning-gemma-2-9b-for-english-to-hinglish-translations\">Hands On Tutorial on Fine Tuning Gemma 2 9B For English to Hinglish Translations<\/h2>\n<p>In the following tutorial, we fine tune the multilingual Gemma 2 9B on a Hinglish Dataset leveraging the Unsloth AI library on Google Colab using T4 GPU. We save the fine tuned model in Hugging Face and then query the model for different inputs through Ollama. Post this, we explore how the fine tuned model helps in more accurate English to Hinglish translations.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-step-1-install-necessary-libraries\">Step 1: Install Necessary Libraries<\/h3>\n<p>We will first install necessary libraries below:<\/p>\n<pre class=\"wp-block-code\"><code>!pip install unsloth<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-2-loading-the-model\">Step 2: Loading the Model<\/h3>\n<p>\u00a0The code below loads the pre-trained Gemma 2 9B language model using the unsloth library. It sets configuration options like a maximum sequence length of 2048 tokens and enables 4-bit quantization to reduce memory usage. The data type (dtype) is auto-detected, and the model and tokenizer are loaded for use in further language processing tasks. This setup optimizes memory efficiency while working with large language models.\u00a0\u00a0<\/p>\n<pre class=\"wp-block-code\"><code>from unsloth import FastLanguageModel\nimport torch\n\nmax_seq_length = 2048  # Choose any! We auto support RoPE Scaling internally!\ndtype = (\n    None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n)\nload_in_4bit = True  # Use 4bit quantization to reduce memory usage. Can be False.\n\nmodel, tokenizer = FastLanguageModel.from_pretrained(\n    model_name=\"unsloth\/gemma-2-9b\",\n    max_seq_length=max_seq_length,\n    dtype=dtype,\n    load_in_4bit=load_in_4bit)\n<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-3-adding-lora-adapters\">Step 3: Adding LoRA Adapters<\/h3>\n<p>For Adding LoRA Adapters, we only need to update 1 to 10% of all parameters. The code below utilizes the\u00a0FastLanguageModel.get_peft_model\u00a0function to adapt a model using LoRA (Low-Rank Adaptation) techniques. It specifies parameters such as the rank (r = 16), target modules for adaptation, and optimization settings like\u00a0lora_alpha\u00a0and\u00a0bias.<\/p>\n<p>The code also enables \u201cunsloth\u201d for efficient memory usage and sets a random state for reproducibility.<\/p>\n<pre class=\"wp-block-code\"><code>model = FastLanguageModel.get_peft_model(\n    model,\n    r = 16, # Choose any number &gt; 0 ! Suggested 8, 16, 32, 64, 128\n    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n    lora_alpha = 16,\n    lora_dropout = 0, # Supports any, but = 0 is optimized\n    bias = \"none\",    # Supports any, but = \"none\" is optimized\n    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n    random_state = 3407,\n    use_rslora = False,  # We support rank stabilized LoRA\n    loftq_config = None, # And LoftQ\n)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-4-defining-the-alpaca-format-for-preparing-the-dataset\">Step 4: Defining the Alpaca Format For Preparing the Dataset<\/h3>\n<p>The code below defines a prompt formatting function for preparing training data in a structured format. It starts by creating a template (alpaca_prompt) that includes placeholders for the instruction, input, and output. The formatting_prompts_func function takes in a batch of examples, extracts the English (en) and Hinglish (hi_ng) text, and formats them into the defined template. It adds an EOS_TOKEN (End-of-Sequence token) at the end of each formatted prompt to prevent the model from generating responses indefinitely. The final output is a dictionary with the formatted text for each example, ready for model training or fine-tuning.<\/p>\n<pre class=\"wp-block-code\"><code>alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{}\n\n### Input:\n{}\n\n### Response:\n{}\"\"\"\n\nEOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\ndef formatting_prompts_func(examples):\n    instructions = [\"Translate English to Hinglish\"]\n    inputs       = examples[\"en\"]\n    outputs      = examples['hi_ng']\n    texts = []\n    for instruction, input, output in zip(instructions, inputs, outputs):\n        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n        texts.append(text)\n    \n    return { \"text\" : texts, }<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-5-loading-the-dataset\">Step 5: Loading the Dataset<\/h3>\n<p>The code below prepares the dataset in the correct format, with each entry consisting of a properly structured instruction-input-output prompt for Hinglish translation tasks.<\/p>\n<pre class=\"wp-block-code\"><code>from datasets import load_dataset\nfrom datasets import Dataset, DatasetDict\n\ndataset = load_dataset(\"nateraw\/english-to-hinglish\", split = \"train\")\ndataset= dataset.remove_columns([\"source\"])\n\ndf_pandas = dataset.to_pandas()\n\ndef apply_format(col1,col2):\n   instruction = \"Translate English to Hinglish\"\n   text = alpaca_prompt.format(instruction, col1, col2) + EOS_TOKEN\n   return text\n   \ndf_pandas['text'] = df_pandas.apply(lambda e:apply_format(e['en'],e['hi_ng']),axis=1)\ndf_pandas.drop(['en','hi_ng'],axis=1,inplace=True)\ndataset = Dataset.from_pandas(df_pandas)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-6-defining-huggingface-trl-s-sfttrainer-for-training-the-model\">Step 6: Defining Huggingface TRL\u2019s\u00a0SFTTrainer for Training the Model<\/h3>\n<p>The code below initializes an SFTTrainer for fine-tuning a model using the trl library. It sets up training parameters such as batch size, gradient accumulation steps, and learning rate within TrainingArguments. The trainer also configures logging and optimization settings, including the use of mixed precision (fp16 or bf16) based on hardware support. The training process is optimized with an AdamW optimizer and a linear learning rate scheduler.<\/p>\n<pre class=\"wp-block-code\"><code>from trl import SFTTrainer\nfrom transformers import TrainingArguments\nfrom unsloth import is_bfloat16_supported\n\ntrainer = SFTTrainer(\n    model = model,\n    tokenizer = tokenizer,\n    train_dataset = dataset,\n    dataset_text_field = \"text\",\n    max_seq_length = max_seq_length,\n    dataset_num_proc = 2,\n    packing = False, # Can make training 5x faster for short sequences.\n    args = TrainingArguments(\n        per_device_train_batch_size = 2,\n        gradient_accumulation_steps = 4,\n        warmup_steps = 5,\n        max_steps = 60,\n        learning_rate = 2e-4,\n        fp16 = not is_bfloat16_supported(),\n        bf16 = is_bfloat16_supported(),\n\n        #LOGGING ARGUMENTS\n        logging_steps = 1,\n        optim = \"adamw_8bit\",\n        weight_decay = 0.01,\n        lr_scheduler_type = \"linear\",\n        seed = 3407,\n        output_dir = \"outputs\",\n        report_to = \"none\", # Use this for WandB etc\n    ),\n)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-7-starting-the-training\">Step 7: Starting the Training<\/h3>\n<pre class=\"wp-block-code\"><code>trainer_stats = trainer.train()<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-8-inference-from-the-fine-tuned-model\">Step 8: Inference from the Fine Tuned Model<\/h3>\n<p>The code below sets up inference for the fine-tuned model using FastLanguageModel. It first prepares a prompt (alpaca_prompt) for translation from English to Hinglish by formatting it with an example input. The prompt is tokenized and transferred to a GPU (cuda) for efficient computation. The model then generates a response with a maximum of 64 new tokens, and the output is decoded back into text. Finally, it extracts the part of the output after the \u201c### Response:\u201d section, which contains the generated Hinglish translation.<\/p>\n<pre class=\"wp-block-code\"><code># alpaca_prompt = Copied from above\nFastLanguageModel.for_inference(model) # Enable native 2x faster inference\ninputs = tokenizer(\n[\n    alpaca_prompt.format(\n        \"Translate English to Hinglish\", # instruction\n        \"remind me to get eggs today\", # input\n        \"\", # output - leave this blank for generation!\n    )\n], return_tensors = \"pt\").to(\"cuda\")\n\noutputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\noutput = tokenizer.batch_decode(outputs)\noutput[0].split(\"### Response:\\n\")[1]<\/code><\/pre>\n<p><strong>Output<\/strong><\/p>\n<pre class=\"wp-block-preformatted\"><br\/>'mujhe aaj eggs lene ke liye yaad dilaayen<eos>'<\/eos><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-step-9-saving-the-model-amp-pushing-to-hugging-face\">Step 9: Saving the Model &amp; Pushing to Hugging Face<\/h3>\n<p>The following code is for saving the trained model and pushing it to Hugging Face Hub. You would need to give it the HF token for writing to the Hub.<\/p>\n<pre class=\"wp-block-code\"><code>model.save_pretrained(\"lora_model\")  # Local saving\ntokenizer.save_pretrained(\"lora_model\")\n\nmodel.push_to_hub(\"mimidutta007\/english_to_hinglish_FTgemma2\", token = \"\") # Online saving\ntokenizer.push_to_hub(\"mimidutta007\/english_to_hinglish_FTgemma2\", token = \"\") # Online saving<\/code><\/pre>\n<p>You can find the model <a href=\"https:\/\/huggingface.co\/mimidutta007\/hinglish_gemma2b\" target=\"_blank\" rel=\"nofollow noopener\">here<\/a>. I have also converted it to GGUF format so that we can query the model through ollama as well.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-querying-the-model-through-ollama\">Querying the Model Through Ollama<\/h2>\n<p>Learn how to interact with the fine-tuned Gemma 2 9B model using Ollama, enabling seamless English-to-Hinglish translations through efficient API queries.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-pulling-the-fine-tuned-model-through-ollama\">Pulling the Fine Tuned Model Through Ollama<\/h3>\n<p>This code installs the Ollama software and the langchain-ollama library, which allows interaction with language models via Ollama. It then starts Ollama as a background subprocess (subprocess.Popen) to run in a non-blocking manner. After waiting for 3 seconds (time.sleep(3)), the code pulls a fine-tuned model (english_to_hinglish_FTgemma2) from Ollama using the ollama pull command. This setup enables the model to be used for English-to-Hinglish translation tasks.<\/p>\n<pre class=\"wp-block-code\"><code>#Installing Ollama and langchain-ollama library\n!curl -fsSL https:\/\/ollama.com\/install.sh | sh\n!pip install langchain-ollama\n\n#Starting a subprocess so that ollama can be run in a non blocking manner\nimport subprocess\nsubprocess.Popen([\"ollama\", \"serve\"])\nimport time\ntime.sleep(3) \n\n#Pulling the Model\n!ollama pull hf.co\/mimidutta007\/english_to_hinglish_FTgemma2<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-querying-the-fine-tuned-model-through-ollama\">Querying the Fine Tuned Model Through Ollama<\/h3>\n<p>This code sets up a prompt template using langchain for an English-to-Hinglish translation task. It defines a template that includes placeholders for the instruction and input, then creates a ChatPromptTemplate from it. The model (OllamaLLM) is instantiated with a fine-tuned Hinglish translation model. The prompt and model are combined in a chain. The input data is passed to the chain, generating a translation response.<br \/>The result is then displayed in Markdown format.<\/p>\n<pre class=\"wp-block-code\"><code>from langchain_core.prompts import ChatPromptTemplate\nfrom langchain_ollama.llms import OllamaLLM\nfrom IPython.display import Markdown\n\n# Define the template\ntemplate = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{Instruction}\n\n### Input:\n{Input}\n\n### Response:\n\"\"\"\n\n# Create a prompt template\nprompt = ChatPromptTemplate.from_template(template)\n# Instantiate the model\nmodel = OllamaLLM(model=\"hf.co\/mimidutta007\/english_to_hinglish_FTgemma2\")\n# Chain the prompt and model\nchain = prompt | model\n\ninput_data = {\n    \"Instruction\": \"Translate from English to Hinglish\",\n    \"Input\": \"are there any roads closed in the area due to construction\"\n}\n\n# Invoke the chain with input data and display the response in Markdown format\nresponse = chain.invoke(input_data)\n<\/code><\/pre>\n<p><b>Output<\/b><\/p>\n<pre class=\"wp-block-preformatted\"><br\/>'kya area ke kisi road par construction ki wajah se band hai'<\/pre>\n<h4 class=\"wp-block-heading\" id=\"h-query-2\">Query-2<\/h4>\n<p><i>\u201cInput\u201d: \u201cplease text Joanne Brennan that I will be five minutes late.\u201d<\/i><\/p>\n<p><b>Output<\/b><\/p>\n<pre class=\"wp-block-preformatted\"><br\/>'Joanne Brenan ko message karo ke main 5 minutes late hoon'<\/pre>\n<h4 class=\"wp-block-heading\" id=\"h-query-3\">Query-3<\/h4>\n<p><i>\u201cInput\u201d: \u201cremind me to get eggs today\u201d<\/i><\/p>\n<p><b>Output<\/b><\/p>\n<pre class=\"wp-block-preformatted\"><br\/>'mujhe aaj eggs lene ke liye yaad dilaayen<eos>'<\/eos><\/pre>\n<h4 class=\"wp-block-heading\" id=\"h-query-4\">Query-4<\/h4>\n<p><i>\u201cInput\u201d: \u201cRotate the Image 90 degrees to the right\u201d<\/i><\/p>\n<p><b>Output<\/b><\/p>\n<pre class=\"wp-block-preformatted\"><br\/>'Image ko 90 degree tak apne Right me rotate kare'<\/pre>\n<p>As we can see from the outputs above, they are translated well into Hinglish. Some words are kept in English, while others are converted to Hindi. This suggests that the model, after training, understands Hinglish grammar and can generate accurate outputs.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-comparison-with-original-gemma-2-9b-model\">Comparison with Original Gemma 2 9B Model<\/h2>\n<p>Lets also check what the original Gemma 2 9B model outputs for an input:<\/p>\n<figure class=\"wp-block-table\">\n<table class=\"table table-bordered border-black table-striped\">\n<thead>\n<tr>\n<th><strong>Input<\/strong><\/th>\n<th><strong>Output From Original Gemma 2 9B<\/strong><\/th>\n<th><strong>Output From Fine-tuned Gemma 2 9B<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>play some motivating music<\/td>\n<td>Karo Yaar Kuch Motivational Gaane<\/td>\n<td>kuch motivate karne wala music bajao<\/td>\n<\/tr>\n<tr>\n<td>play some motivating music<\/td>\n<td>Chal yaar, kucch pumping gaane bajao<\/td>\n<td>kuch motivate karne wali music play kare<\/td>\n<\/tr>\n<tr>\n<td>remind me to get eggs today<\/td>\n<td>Yaar, mujhe aaj ande lene ka yaad dila de!<\/td>\n<td>mujhe aaj eggs lene ke liye yaad dilaayen<\/td>\n<\/tr>\n<tr>\n<td>please text Joanne Brennan that I will be five<\/td>\n<td>Bhai Joanne Brennan ko msg kar de ki main 5 minute late ho jaunga.<\/td>\n<td>Joanne Brennan ko message karo ki main 5 minutes late ho<\/td>\n<\/tr>\n<tr>\n<td>Request you to please give feedback on comment section<\/td>\n<td>Kya comment section mein kya chal raha hai? Bata de feedback do!<\/td>\n<td>Mujhe comment section par apne feedback dene ki request hai<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<ul class=\"wp-block-list\">\n<li>As we see in the table above, the original Gemma 2 9B outputs are not contextually incorrect. However, the fine-tuned model provides contextually accurate responses. It also maintains a formal tone in the message. In contrast, the original model\u2019s output sounds more casual.<\/li>\n<li>Also, some outputs from the original model are not Hinglish but in complete Hindi like \u201cYaar, mujhe aaj ande lene ka yaad dila de!\u201d<\/li>\n<li>We also observe some contextuallu inaccurate translations by the original Gemma 2 9B model like \u201cKya comment section mein kya chal raha hai? Bata de feedback do!\u201d while the fine tuned model translates it accurately.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>The development of LLM models for Hinglish translation is crucial for bridging the gap between formal languages and the hybrid dialect commonly used in India\u2019s everyday communication. Fine-tuning the multilingual Gemma 2 9B model offers significant advantages, especially with its efficiency, multilingual strengths, and adaptability to Hinglish\u2019s unique nuances. This approach not only enhances translation accuracy but also facilitates better communication in personal and professional contexts. With the support of Unsloth AI\u2019s innovative fine-tuning capabilities, this model can revolutionize Hinglish translation and improve engagement across diverse audiences.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-key-takeaways\">Key Takeaways<\/h3>\n<ul class=\"wp-block-list\">\n<li>Hinglish, a blend of Hindi and English, is increasingly used in informal communication across India. Hence making it essential for businesses and individuals to develop accurate translation models to engage with a broader audience effectively.<\/li>\n<li>The Gemma 2 9B model is compact yet powerful, with 9 billion parameters and excellent multilingual capabilities. It excels in various tasks such as text generation, code writing, and problem-solving, making it highly versatile.<\/li>\n<li>Fine-tuning the Gemma 2 9B model on Hinglish datasets improves its translation accuracy and ensures it adapts to Hinglish\u2019s unique syntax, grammar, and cultural nuances, making it more effective for real-world applications.<\/li>\n<li>The Gemma 2 9B model\u2019s smaller size (9 billion parameters) allows for efficient deployment on devices with limited resources, offering high performance without the need for costly hardware.<\/li>\n<li>Unsloth AI\u2019s platform significantly enhances the fine-tuning process by enabling faster training (up to 30 times faster) with 90% less memory usage, making AI training more accessible and cost-effective for developers.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1741604989824\"><strong class=\"schema-faq-question\">Q1. <b>Why is it important to develop LLM models for Hinglish translation?<\/b><\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Hinglish, a blend of Hindi and English, is widely used in informal communication in India, especially on social media, in advertising, and in daily conversations. Developing LLM models for Hinglish translation helps businesses and individuals effectively communicate with a broader audience, improving engagement and bridging the gap between formal and colloquial language.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1741605005294\"><strong class=\"schema-faq-question\">Q2. <b>What is the Gemma 2 9B model, and how does it support Hinglish translation?<\/b><\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. The Gemma 2 9B model is a powerful language processing tool with 9 billion parameters, offering robust performance across multilingual tasks. Its compact size, high efficiency, and adaptability make it an ideal candidate for fine-tuning on Hinglish datasets, improving translation accuracy and capturing Hinglish\u2019s unique syntax and cultural nuances.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1741605018208\"><strong class=\"schema-faq-question\">Q3. <b>How does fine-tuning the Gemma 2 9B model improve Hinglish translation?<\/b><\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Fine-tuning the Gemma 2 9B model using curated Hinglish datasets allows the model to adapt to the language\u2019s distinct syntax, grammar, and vocabulary. This customization ensures more accurate and culturally relevant translations from English to Hinglish, improving communication in both personal and professional contexts.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1741605043076\"><strong class=\"schema-faq-question\">Q4. <b>What are the advantages of using Unsloth AI for fine-tuning?<\/b><\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Unsloth AI offers significant advantages by enabling faster training (up to 30 times faster) while using 90% less memory than traditional methods. This platform makes the fine-tuning process more efficient, cost-effective, and accessible, helping developers create highly specialized language models with fewer resources.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><strong>The media shown in this article is not owned by Analytics Vidhya and is used at the Author\u2019s discretion.<\/strong><\/p>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/mimi6\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_ZkJo4gb.webp\" width=\"48\" height=\"48\" alt=\"Nibedita Dutta\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Nibedita completed her master\u2019s in Chemical Engineering from IIT Kharagpur in 2014 and is currently working as a Senior Data Scientist. In her current capacity, she works on building intelligent ML-based solutions to improve business processes.               <\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Have you ever thought about how to make communication easier for people who use a mix of Hindi and English, commonly known as Hinglish? With the growing use of Hinglish in everyday conversations, social media, and advertising, there\u2019s a need for tools that can accurately translate between English and Hinglish. This is where advanced language [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":127817,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[5815,55863,23318,55862,6196],"dealstore":[],"offerexpiration":[],"class_list":["post-127816","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-blogathon","tag-englishtohinglish","tag-gemma","tag-refining","tag-translations"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Refining English-to-Hinglish Translations with Gemma 2 9B - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=127816\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Refining English-to-Hinglish Translations with Gemma 2 9B - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Have you ever thought about how to make communication easier for people who use a mix of Hindi and English, commonly known as Hinglish? 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