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