This modelcard aims to be a base template for new models. It has been generated using this raw template.

This model is a fine-tuned FastLlama (Llama 3) model designed for English-to-Hindi colloquial translation.

🔹 What It Does: Translates English text into Hindi (colloquial form) Uses Llama 3-8B (or smaller) with FastLlama for efficient inference Optimized with quantization (4-bit) for faster performance Fine-tuned on your custom dataset (bajpaideeksha/english-hindi-colloquial-dataset)

🔹 How It Works: 1️⃣ User inputs English text 2️⃣ Model processes the text using your fine-tuned dataset 3️⃣ Generates colloquial Hindi output

This model is designed for English-to-Hindi colloquial translation, making it useful for:

Conversational AI – Chatbots, virtual assistants Language Learning – Helping learners understand informal Hindi Content Localization – Adapting English content to Hindi for social media, customer support, etc.

When used without fine-tuning, the base unsloth/llama-3-8b model operates as a general-purpose language model with capabilities such as:

General text generation (summarization, storytelling, Q&A) Basic translation (but not optimized for colloquial Hindi) Code generation and creative writing Conversational AI with broad knowledge

Misinformation & Bias – The model may generate inaccurate or biased translations, especially in sensitive contexts. Hate Speech & Abuse – Could be misused to generate harmful or offensive language. Impersonation & Fraud – Could be misused in deceptive applications like fake translations or misinformation campaigns.

[More Information Needed]

⚙️ Technical Limitations Memory Constraints – Requires significant GPU VRAM; may struggle on low-end devices. Inference Speed – Despite 4-bit quantization, response time may still be slow for large inputs. Hallucinations – May generate incorrect or nonsensical translations, especially in complex contexts. Limited Context Window – Cannot effectively handle very long conversations (truncates input). 🌍 Sociotechnical Limitations Cultural Nuances – Colloquial Hindi varies by region, and the model may not always reflect this accurately. Bias in Training Data – If the dataset has biases, the model may produce stereotypical or inaccurate translations. Ethical Risks – Could reinforce harmful narratives if not monitored properly. Accessibility Concerns – May not work well for users unfamiliar with Hinglish or those needing formal Hindi.

The model was fine-tuned on bajpaideeksha/english-hindi-colloquial-dataset, which contains English sentences and their colloquial Hindi translations. The dataset was preprocessed to ensure correct input-output mappings for better translation accuracy.

bajpaideeksha/english-hindi-colloquial-dataset

🛠️ Fine-Tuning Process Base Model: unsloth/llama-3-8b (Llama 3-based) Fine-Tuning Framework: FastLlama (by Unsloth) Quantization: 4-bit for efficient memory usage Sequence Length: 1024-2048 tokens (adjustable) Training Parameters: Batch Size: 4 per device Gradient Accumulation: 8 steps Learning Rate: 2e-5 Epochs: 3 Optimizer: AdamW 🖥️ Hardware Used GPU: A100 / 3090 / Similar high-memory GPU Memory Considerations: Requires at least 24GB VRAM for smooth training 📊 Evaluation Metrics BLEU Score – Measures translation accuracy Perplexity (PPL) – Evaluates model fluency Manual Review – Checking translations for naturalness and correctness

📊 Evaluation Metrics BLEU Score – Measures translation accuracy Perplexity (PPL) – Evaluates model fluency Manual Review – Checking translations for naturalness and correctness

📊 Testing Data Dataset Used: bajpaideeksha/english-hindi-colloquial-dataset Split Ratio: Training Set: 80% Validation Set: 10% Test Set: 10% Size: Approx. X number of English-Hindi sentence pairs (replace with actual count) Diversity: Includes casual, slang, and regional Hindi variations 📌 Factors Considered Accuracy & Fluency – Ensuring translations are natural and contextually correct Colloquial Consistency – Maintaining informal, everyday language usage Inference Speed – Measuring latency per translation request Handling of Code-Switching – Evaluating how well it translates Hinglish (mixed Hindi-English text) Bias & Fairness – Checking for stereotypical, offensive, or biased translations 📉 Evaluation Metrics BLEU Score – Measures translation quality Perplexity (PPL) – Assesses model fluency Human Evaluation – Native Hindi speakers rate translations for accuracy & naturalness Response Time (ms) – Measures real-time translation speed

📌 Overview This model is a fine-tuned version of unsloth/llama-3-8b, optimized for English-to-Hindi colloquial translation. It enables informal, everyday Hindi translations, making it useful for casual conversations, social media, and chatbot applications.

🛠️ Training & Technical Details Base Model: unsloth/llama-3-8b (Llama 3-based) Fine-Tuned On: bajpaideeksha/english-hindi-colloquial-dataset Quantization: 4-bit (Efficient memory usage) Training Hardware: NVIDIA A100 / RTX 3090 with at least 24GB VRAM Evaluation Metrics: BLEU Score, Perplexity (PPL), Human Review ✅ Capabilities Converts English text into casual, colloquial Hindi Handles slang, informal phrases, and Hinglish Can be used for chatbots, translation tools, and language learning ⚠️ Limitations & Considerations Not suitable for formal/legal documents May struggle with complex technical terms Potential biases due to training data 🌍 Environmental Impact Estimated CO₂ Emissions: 50-100 kg CO₂eq per training run Energy Usage: ~2-5 kWh per training hour

🌍 Total Emissions & Energy Usage Estimated CO₂ Emissions: Varies based on training duration and hardware Single GPU Training (A100, 24GB VRAM) → ~50-100 kg CO₂eq per full fine-tuning run Inference (Per 1M Tokens) → ~10-20 g CO₂eq Electricity Usage: Training Phase: ~2-5 kWh per hour Inference: Minimal, depending on usage frequency ⚡ Mitigation Strategies Used 4-bit quantization to reduce compute requirements Leveraged efficient cloud instances with lower energy consumption Can be deployed on renewable-energy-powered servers

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

🖥️ Hardware Used GPU: NVIDIA A100 / RTX 3090 (or equivalent high-memory GPU) VRAM Requirement: At least 24GB for smooth training and inference CPU: High-performance multi-core processor (e.g., AMD EPYC, Intel Xeon) RAM: 64GB+ recommended for efficient data processing Storage: SSD (1TB+) for dataset handling and model checkpoints ⚡ Frameworks & Libraries Transformers (Hugging Face) – For model loading & fine-tuning FastLlama (Unsloth) – For optimized inference & training Bitsandbytes – Enables 4-bit quantization PyTorch – Deep learning framework Accelerate – Distributed training and optimization ⚙️ Optimization Techniques 4-bit Quantization – Reduces memory usage Gradient Accumulation – Helps with training on smaller GPUs Mixed Precision (FP16/BF16) – Improves efficiency

🖥️ Hardware Used GPU: A100 / 3090 / Similar high-memory GPU Memory Considerations: Requires at least 24GB VRAM for smooth training