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bloomz-1b1MM
LinoM/bloomz-1b1MM
bloomz-1b1MM is a translation model from LinoM. Use it when you need text moved from one language to another. It is set up for peft. The card lists the license as apache-2.0.
Model Name: LinoM/bloomz-1b1MM Base Model: bigscience/bloomz-1b1 Fine-Tuning Method: QLoRA (4-bit LoRA adapters + 8-bit base model) Frameworks: Hugging Face Transformers + PEFT + BitsAndBytes Task: English to Myanmar…
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Updated Aug 7, 2025
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At a glance
- Task
- Translation
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- 6543f499
Try a prompt
Base models
Model card
From the Hugging Face model README
🌸 BloomZ-1.1B LoRA Fine-tuned for English → Myanmar (Burmese) Translation
Model Name: LinoM/bloomz-1b1MM
Base Model: bigscience/bloomz-1b1
Fine-Tuning Method: QLoRA (4-bit LoRA adapters + 8-bit base model)
Frameworks: Hugging Face Transformers + PEFT + BitsAndBytes
Task: English to Myanmar Instruction-style Translation
🧠 Model Details
| Detail | Value |
|---|---|
| Model Architecture | BLOOMZ |
| Base Model Size | 1.1 Billion Parameters |
| Fine-tuning Method | LoRA with QLoRA (4-bit adapters) |
| Optimizer | paged_adamw_8bit |
| Precision | 4-bit LoRA + 8-bit Base |
| Epochs | 3–5 (variable per run) |
| Batch Size | 32 |
| Language Pair | English → Burmese (မြန်မာ) |
| Tokenizer | Bloom tokenizer (bigscience/tokenizer) |
📚 Training Data
The model was fine-tuned on a curated mix of open datasets including:
- 🌍 FLORES200 (en–my)
- 🎬 OpenSubtitles (Movie subtitles in Myanmar)
- 📖 Custom Instruction-style translation datasets (8 use cases, 200+ pairs per use case)
- 🗣️ ai4bharat/indictrans2-en-my (additional Burmese corpora)
📈 Evaluation
| Metric | Score |
|---|---|
| BLEU | 35–40 |
| Translation Style | Instructional, formal |
| Human Evaluation | ✓ Understood grammar and tone in 85% samples |
✅ The model excels at translating English prompts into formal Burmese suitable for education, scripts, and user guides.
🔧 How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-1b1", load_in_8bit=True, device_map="auto")
lora = PeftModel.from_pretrained(base, "LinoM/bloomz-1b1MM")
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-1b1")
translator = pipeline("text-generation", model=lora, tokenizer=tokenizer)
text = "Translate into Burmese: What is your favorite subject?"
output = translator(text, max_new_tokens=100)
print(output[0]['generated_text'])
- Task
- Translation
- Library
- peft
- License
- apache-2.0
- Languages
- en, my
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025