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--- |
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base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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- multilingual |
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- instruction-tuning |
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license: apache-2.0 |
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language: |
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- en |
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- kn |
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datasets: |
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- charanhu/kannada-instruct-dataset-390k |
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library_name: transformers |
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--- |
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# Uploaded Model: devshaheen/llama-3.2-3b-Instruct-finetune |
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## Overview |
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- **Developed by:** devshaheen |
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- **License:** Apache-2.0 |
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- **Finetuned from model:** `unsloth/llama-3.2-3b-instruct-bnb-4bit` |
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- **Languages Supported:** |
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- **English** (`en`) for general-purpose text generation and instruction-following tasks. |
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- **Kannada** (`kn`) with a focus on localized and culturally aware text generation. |
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- **Dataset Used:** [charanhu/kannada-instruct-dataset-390k](https://huggingface.co/datasets/charanhu/kannada-instruct-dataset-390k) |
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This model is a fine-tuned version of LLaMA, optimized for multilingual instruction-following tasks with a specific emphasis on English and Kannada. It utilizes 4-bit quantization for efficient deployment in low-resource environments without compromising performance. |
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## Features |
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### 1. **Instruction Tuning** |
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The model is trained to follow a wide range of instructions and generate contextually relevant responses. It excels in both creative and factual text generation tasks. |
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### 2. **Multilingual Support** |
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The model is capable of generating text in Kannada and English, making it suitable for users requiring bilingual capabilities. |
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### 3. **Optimized Training** |
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Training was accelerated using [Unsloth](https://github.com/unslothai/unsloth), achieving **2x faster training** compared to conventional methods. This was complemented by HuggingFace's TRL (Transformers Reinforcement Learning) library to ensure high performance. |
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### 4. **Efficiency through Quantization** |
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Built on the `bnb-4bit` quantized model, it is designed for optimal performance in environments with limited computational resources while maintaining precision and depth in output. |
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## Usage Scenarios |
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### General Use |
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- Text completion and creative writing. |
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- Generating instructions or following queries in English and Kannada. |
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### Specialized Applications |
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- Localized AI systems in Kannada for chatbots, educational tools, and more. |
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- Research and development on multilingual instruction-tuned models. |
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## Performance and Metrics |
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### Evaluation Dataset: |
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The model was fine-tuned on [charanhu/kannada-instruct-dataset-390k](https://huggingface.co/datasets/charanhu/kannada-instruct-dataset-390k), a comprehensive dataset designed for Kannada instruction tuning. |
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### Training Parameters: |
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- **Base Model:** LLaMA 3.2-3B-Instruct |
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- **Optimizer:** AdamW |
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- **Quantization:** 4-bit (bnb-4bit) |
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- **Framework:** HuggingFace Transformers + Unsloth |
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--- |
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## Example Usage |
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### Python Code |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load model and tokenizer |
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model_name = "devshaheen/llama-3.2-3b-Instruct-finetune" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Generate text |
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input_text = "How does climate change affect the monsoon in Karnataka?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=150) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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