File size: 2,906 Bytes
178eebc 82b207a 178eebc 82b207a 178eebc 82b207a 178eebc 82b207a 178eebc 530b6c0 178eebc 82b207a 178eebc 82b207a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- Tulu3
- Smollm
- SLMs
- Small
- Huggingface
- Allenai
- SFT
- DPO
- GGUF
- RLVR
- RL
base_model:
- SultanR/SmolTulu-1.7b-Instruct
datasets:
- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
pipeline_tag: text-generation
---
# SmolLM2 1.7b Aligned and Reinforced Through Tulu 3!
SmolTulu-1.7b-Reinforced is the reinforcement learning with verifiable rewards (RLVR) version of [SmolTulu-1.7b-Instruct](https://huggingface.co/SultanR/SmolTulu-1.7b-Instruct), which leverages [AllenAI's Tulu 3 post-training pipeline](https://arxiv.org/abs/2411.15124)
This model scores the highest current score in both IFEval and GSM8k while maintaining the extremely low contamination levels in Tulu 3 and SmolLM2! I've listed the datasets used to do both the RLVR stage, which is the same one mentioned used in the Tulu 3 paper.
## Evaluation
I ran these evaluations using [SmolLM2's evaluation code](https://github.com/huggingface/smollm/tree/main/evaluation) for a more fair comparison.
| Metric | SmolTulu-1.7b-Instruct | SmolTulu-1.7b-Reinforced | SmolLM2-1.7B-Instruct | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct | SmolLM1-1.7B-Instruct |
|:----------------------------|:---------------------:|:---------------------:|:---------------------:|:---------------------:|:---------------------:|:---------------------:|
| ARC (Average) | 51.5 | 51.1 | **51.7** | 41.6 | 46.2 | 43.7 |
| BBH (3-shot) | 33.8 | 33.4 | 32.2 | 27.6 | **35.3** | 25.7 |
| GSM8K (5-shot) | 51.6 | **61.0** | 48.2 | 26.8 | 42.8 | 4.6 |
| HellaSwag | 61.1 | 60.4 | **66.1** | 56.1 | 60.9 | 55.5 |
| IFEval (Average prompt/inst) | 67.7 | **69.3** | 56.7 | 53.5 | 47.4 | 23.1 |
| MMLU-Pro (MCF) | 17.4 | 17.3 | 19.3 | 12.7 | **24.2** | 11.7 |
| PIQA | 72.2 | 72.1 | **74.4** | 72.3 | 73.2 | 71.6 |
## Usage
Just like any Huggingface model, just run it using the transformers library:
```python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "SultanR/SmolTulu-1.7b-Reinforced"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
## Citation
```
@misc{alrashed2024smoltuluhigherlearningrate,
title={SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs},
author={Sultan Alrashed},
year={2024},
eprint={2412.08347},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.08347},
}
``` |