| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - mteb/imdb |
| | - Helsinki-NLP/opus-100 |
| | language: |
| | - en |
| | - fr |
| | pipeline_tag: text-classification |
| | base_model: |
| | - TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| | tags: |
| | - unsloth |
| | - lora |
| | - peft |
| | - Multi-Task |
| | - Sentiment Analysis |
| | - Translation (English to French) |
| | --- |
| | # TinyLlama Multi-Task LoRA (Sentiment + Translation) |
| |
|
| | This repository contains a **LoRA adapter** trained on top of |
| | **TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T** |
| | to support **multiple tasks** via instruction-style prompting. |
| |
|
| | --- |
| |
|
| | ## 🔧 Base Model |
| |
|
| | - **Base model**: `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T` |
| | - **Architecture**: Decoder-only (LLaMA-style) |
| | - **Fine-tuning method**: LoRA (PEFT) |
| | - **Trainable parameters**: ~4M |
| | - **Total parameters**: ~1.1B |
| |
|
| | This repository **only contains the LoRA adapter weights**, not the full base model. |
| |
|
| | --- |
| |
|
| | ## 📌 Supported Tasks |
| |
|
| | ### 1️⃣ Sentiment Analysis (IMDB) |
| |
|
| | Binary sentiment classification for movie reviews: |
| |
|
| | - `positive` |
| | - `negative` |
| |
|
| | ### 2️⃣ Translation (English → French) |
| |
|
| | Neural machine translation from English to French. |
| |
|
| | --- |
| |
|
| | ## 🧠 Training Data |
| |
|
| | All datasets are loaded directly from **Hugging Face Datasets**: |
| |
|
| | | Task | Dataset | Description | |
| | | ------------------- | ------------------------------- | -------------------------------- | |
| | | Sentiment Analysis | `imdb` | Movie reviews with binary labels | |
| | | Translation (EN→FR) | `Helsinki-NLP/opus-100` (en-fr) | Parallel English–French corpus | |
| |
|
| | --- |
| |
|
| | ## 🧩 Training Strategy |
| |
|
| | - **Multi-task instruction tuning** |
| | - Tasks are distinguished via **explicit prompt headers** |
| | - All tasks are unified into a **causal language modeling** objective |
| | - Only LoRA parameters are updated; base model weights remain frozen |
| |
|
| | --- |
| |
|
| | ## 🧾 Prompt Format |
| |
|
| | ### Sentiment Analysis |
| |
|
| | ```text |
| | ### Task: Sentiment Analysis |
| | ### Review: |
| | This movie was absolutely fantastic! |
| | ### Answer: |
| | positive |
| | |
| | ### Task: Translation (English to French) |
| | ### English: |
| | I love learning large language models. |
| | ### French: |
| | J'aime apprendre les grands modèles de langage. |
| | |
| | |
| | ``` |
| |
|
| | **Load Base Model + LoRA Adapter** |
| |
|
| | ```python |
| | import torch |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | from peft import PeftModel |
| | |
| | base_model_name = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T" |
| | lora_repo = "BEncoderRT/tinyllama-multitask-lora" |
| | |
| | tokenizer = AutoTokenizer.from_pretrained(base_model_name) |
| | tokenizer.pad_token = tokenizer.eos_token |
| | |
| | base_model = AutoModelForCausalLM.from_pretrained( |
| | base_model_name, |
| | device_map="auto" |
| | ).eval() |
| | |
| | lora_model = AutoModelForCausalLM.from_pretrained( |
| | base_model_name, |
| | device_map="auto" |
| | ) |
| | |
| | lora_model = PeftModel.from_pretrained(lora_model, lora_repo) |
| | lora_model.eval() |
| | |
| | def generate( |
| | model, |
| | prompt, |
| | max_new_tokens=64, |
| | temperature=0.3 |
| | ): |
| | inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| | |
| | with torch.no_grad(): |
| | outputs = model.generate( |
| | **inputs, |
| | max_new_tokens=max_new_tokens, |
| | do_sample=True, |
| | temperature=temperature, |
| | pad_token_id=tokenizer.eos_token_id |
| | ) |
| | |
| | return tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | ``` |
| |
|
| |
|
| |
|
| | **Inference Examples** |
| |
|
| | Sentiment Analysis Example |
| |
|
| | ```python |
| | sentiment_tests = [ |
| | "I absolutely loved this movie. The acting was brilliant.", |
| | "This film was boring, slow, and a complete waste of time.", |
| | "The movie was okay, but nothing special.", |
| | ] |
| | |
| | def test_sentiment_comparison(texts): |
| | for text in texts: |
| | prompt = ( |
| | "### Task: Sentiment Analysis\n" |
| | "### Review:\n" |
| | f"{text}\n" |
| | "### Answer:\n" |
| | ) |
| | |
| | base_out = generate(base_model, prompt, max_new_tokens=8) |
| | lora_out = generate(lora_model, prompt, max_new_tokens=8) |
| | |
| | print("=" * 80) |
| | print("REVIEW:") |
| | print(text) |
| | print("\n[BASE MODEL OUTPUT]") |
| | print(base_out) |
| | print("\n[LORA MODEL OUTPUT]") |
| | print(lora_out) |
| | ``` |
| |
|
| | ```python |
| | test_sentiment_comparison(sentiment_tests) |
| | ``` |
| |
|
| | ``` |
| | ================================================================================ |
| | REVIEW: |
| | I absolutely loved this movie. The acting was brilliant. |
| |
|
| | [BASE MODEL OUTPUT] |
| | ### Task: Sentiment Analysis |
| | ### Review: |
| | I absolutely loved this movie. The acting was brilliant. |
| | ### Answer: |
| | I loved this movie. It was so |
| |
|
| | [LORA MODEL OUTPUT] |
| | ### Task: Sentiment Analysis |
| | ### Review: |
| | I absolutely loved this movie. The acting was brilliant. |
| | ### Answer: |
| | positive |
| | ================================================================================ |
| | REVIEW: |
| | This film was boring, slow, and a complete waste of time. |
| |
|
| | [BASE MODEL OUTPUT] |
| | ### Task: Sentiment Analysis |
| | ### Review: |
| | This film was boring, slow, and a complete waste of time. |
| | ... |
| | ### Review: |
| | The movie was okay, but nothing special. |
| | ### Answer: |
| | negative |
| | ``` |
| | |
| | |
| | |
| | |
| | |
| | Translation Example |
| | |
| | ```python |
| | translation_tests = [ |
| | |
| | "I love learning large language models.", |
| | |
| | "This movie was disappointing and boring.", |
| | |
| | "Artificial intelligence is changing the world." |
| | |
| | ] |
| | def test_translation_comparison(texts): |
| | for text in texts: |
| | prompt = ( |
| | "### Task: Translation (English to French)\n" |
| | "### English:\n" |
| | f"{text}\n" |
| | "### French:\n" |
| | ) |
| | |
| | base_out = generate(base_model, prompt, max_new_tokens=64) |
| | lora_out = generate(lora_model, prompt, max_new_tokens=64) |
| | |
| | print("=" * 80) |
| | print("ENGLISH:") |
| | print(text) |
| | print("\n[BASE MODEL OUTPUT]") |
| | print(base_out) |
| | print("\n[LORA MODEL OUTPUT]") |
| | print(lora_out) |
| | |
| | ``` |
| | |
| | ```python |
| | test_sentiment_comparison(sentiment_tests) |
| | ``` |
| | |
| | ``` |
| | ================================================================================ |
| | ENGLISH: |
| | I love learning large language models. |
| | |
| | [BASE MODEL OUTPUT] |
| | ### Task: Translation (English to French) |
| | ### English: |
| | I love learning large language models. |
| | ### French: |
| | J'adore apprendre les modèles de langage grand. |
| | |
| | ### Translation: |
| | I love learning large language models. |
| | ### Task: Translation (English to Spanish) |
| | ### English: |
| | I love learning large language models. |
| | ### Spanish: |
| | Me gusta |
| | |
| | [LORA MODEL OUTPUT] |
| | ### Task: Translation (English to French) |
| | ### English: |
| | I love learning large language models. |
| | ### French: |
| | Je me passionne pour les modèles de langues grandes. |
| | ================================================================================ |
| | ... |
| | ### English: |
| | Artificial intelligence is changing the world. |
| | ### French: |
| | L'intelligence artificielle change le monde. |
| | ``` |
