|
|
--- |
|
|
library_name: transformers |
|
|
license: apache-2.0 |
|
|
language: |
|
|
- en |
|
|
- fr |
|
|
- es |
|
|
- it |
|
|
- pt |
|
|
- zh |
|
|
- ar |
|
|
- ru |
|
|
base_model: |
|
|
- HuggingFaceTB/SmolLM3-3B-Base |
|
|
tags: |
|
|
- openvino |
|
|
- int4 |
|
|
- quantization |
|
|
- edge-deployment |
|
|
- optimization |
|
|
- smollm3 |
|
|
inference: false |
|
|
--- |
|
|
|
|
|
# SmolLM3 INT4 OpenVINO |
|
|
|
|
|
## 🚀 Optimized for Edge Deployment |
|
|
|
|
|
This is an INT4 quantized version of [SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) using OpenVINO, designed for efficient inference on edge devices and CPUs. |
|
|
|
|
|
## Model Overview |
|
|
|
|
|
- **Base Model:** SmolLM3-3B |
|
|
- **Quantization:** INT4 via OpenVINO |
|
|
- **Size Reduction:** Significant compression achieved |
|
|
- **Target Hardware:** CPUs, Intel GPUs, NPUs |
|
|
- **Use Cases:** Local inference, edge deployment, resource-constrained environments |
|
|
|
|
|
## 🔧 Technical Details |
|
|
|
|
|
### Quantization Process |
|
|
```python |
|
|
# Quantized using OpenVINO NNCF |
|
|
# INT4 symmetric quantization |
|
|
# Calibration dataset: [specify if used] |
|
|
``` |
|
|
|
|
|
### Model Architecture |
|
|
- Same architecture as SmolLM3-3B |
|
|
- GQA and NoPE preserved |
|
|
- 64k context support (128k with YARN) |
|
|
- Multilingual capabilities maintained |
|
|
|
|
|
## 📊 Performance (Experimental) |
|
|
|
|
|
> ⚠️ **Note:** This is an experimental quantization. Formal benchmarks pending. |
|
|
|
|
|
Expected benefits of INT4 quantization: |
|
|
- Reduced model size |
|
|
- Faster CPU inference |
|
|
- Lower memory requirements |
|
|
- Some quality trade-off |
|
|
|
|
|
Actual metrics will be added after proper benchmarking. |
|
|
|
|
|
## 🛠️ How to Use |
|
|
|
|
|
### Installation |
|
|
```bash |
|
|
pip install optimum[openvino] transformers |
|
|
``` |
|
|
|
|
|
### Basic Usage |
|
|
```python |
|
|
from optimum.intel import OVModelForCausalLM |
|
|
from transformers import AutoTokenizer |
|
|
|
|
|
model_id = "dev-bjoern/smollm3-int4-ov" |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
|
model = OVModelForCausalLM.from_pretrained(model_id) |
|
|
|
|
|
# Generate text |
|
|
prompt = "Explain quantum computing in simple terms" |
|
|
inputs = tokenizer(prompt, return_tensors="pt") |
|
|
outputs = model.generate(**inputs, max_new_tokens=100) |
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
|
|
``` |
|
|
|
|
|
### With Extended Thinking |
|
|
```python |
|
|
messages = [ |
|
|
{"role": "system", "content": "/think"}, |
|
|
{"role": "user", "content": "Solve this step by step: 25 * 16"} |
|
|
] |
|
|
|
|
|
text = tokenizer.apply_chat_template( |
|
|
messages, |
|
|
tokenize=False, |
|
|
add_generation_prompt=True |
|
|
) |
|
|
``` |
|
|
|
|
|
## 🎯 Intended Use |
|
|
|
|
|
- **Edge AI applications** |
|
|
- **Local LLM deployment** |
|
|
- **Resource-constrained environments** |
|
|
- **Privacy-focused applications** |
|
|
- **Offline AI assistants** |
|
|
|
|
|
## ⚡ Optimization Tips |
|
|
|
|
|
1. **CPU Inference:** Use OpenVINO runtime for best performance |
|
|
2. **Batch Processing:** Consider batching requests when possible |
|
|
3. **Memory:** INT4 significantly reduces memory requirements |
|
|
|
|
|
## 🧪 Experimental Status |
|
|
|
|
|
This is my first experiment with OpenVINO INT4 quantization. Feedback and contributions are welcome! |
|
|
|
|
|
### Known Limitations |
|
|
- No formal benchmarks yet |
|
|
- Quantization settings not fully optimized |
|
|
- Some quality degradation vs full precision |
|
|
|
|
|
### Future Improvements |
|
|
- [ ] Comprehensive benchmarking |
|
|
- [ ] Mixed precision experiments |
|
|
- [ ] Model compression analysis |
|
|
- [ ] Calibration dataset optimization |
|
|
|
|
|
## 🤝 Contributing |
|
|
|
|
|
Found issues or have suggestions? Please open a discussion or issue! |
|
|
|
|
|
## 📚 Resources |
|
|
|
|
|
- [Original SmolLM3 Model](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) |
|
|
- [OpenVINO Documentation](https://docs.openvino.ai/) |
|
|
- [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) |
|
|
|
|
|
## 🙏 Acknowledgments |
|
|
|
|
|
- HuggingFace team for SmolLM3 |
|
|
- Intel OpenVINO team for quantization tools |
|
|
- Community for feedback and support |
|
|
|
|
|
## 📝 Citation |
|
|
|
|
|
If you use this model, please cite both the original and this work: |
|
|
|
|
|
```bibtex |
|
|
@misc{smollm3-int4-ov, |
|
|
author = {Bjoern Bethge}, |
|
|
title = {SmolLM3 INT4 OpenVINO}, |
|
|
year = {2024}, |
|
|
publisher = {Hugging Face}, |
|
|
howpublished = {\url{https://huggingface.co/dev-bjoern/smollm3-int4-ov}} |
|
|
} |
|
|
``` |
|
|
|
|
|
--- |
|
|
|
|
|
**Status:** 🧪 Experimental | **Feedback:** Welcome | **License:** Apache 2.0 |
