SultanR
/

SmolTulu-1.7b-RM

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 library_name: transformers
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 ---
+license: apache-2.0
+language:
+- en
 library_name: transformers
+tags:
+- Tulu3
+- Smollm
+- SLMs
+- Small
+- Huggingface
+- Allenai
+- Reward Model
+- RLVR
+- RM
+- Reward
+base_model:
+- SultanR/SmolTulu-1.7b-Instruct
+datasets:
+- allenai/tulu-3-sft-mixture
+- allenai/llama-3.1-tulu-3-8b-preference-mixture
+pipeline_tag: text-generation
 ---
+# SmolLM2 1.7b Reward Model for RLVR Through Tulu 3!
+![SmolTulu Banner](smoltulubanner.png)
+SmolTulu-1.7b-RM is the reward model used to initialize the value function for [SmolTulu-1.7b-Reinforced](https://huggingface.co/SultanR/SmolTulu-1.7b-Reinforced), which leverages [AllenAI's Tulu 3 post-training pipeline](https://arxiv.org/abs/2411.15124) for reinforcement learning with verifiable rewards (RLVR). This model was trained using the same preference datasets and methodology as outlined in the Tulu 3 paper, adapted for the smaller model size.
 ## Evaluation
+Evaluation results comparing SmolTulu-1.7b-RM against the Tulu 3 8b reward model on standard reward model benchmarks:
+| Metric | SmolTulu-1.7b-RM | Tulu 3 8b RM |
+|:-----------|:----------------:|:-------------:|
+| RB Chat | *94.13* | **96.27** |
+| RB Chat Hard | 43.64 | **55.92** |
+| RB Safety | *75.54* | **84.05** |
+| RB Reasoning | *68.01* | **76.50** |
+| RB Average | *72.43* | **81.34** |
+| UFB | *73.17* | **77.34** |
+While the 1.7B reward model shows lower performance compared to the larger 8B model as expected, it still demonstrates strong capabilities across different evaluation categories, particularly in chat quality assessment.
+## Usage
+The reward model can be used with the transformers library:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+checkpoint = "SultanR/SmolTulu-1.7b-RM"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint).to(device)
+# Example of computing reward for a completion
+def get_reward(prompt, completion):
+    inputs = tokenizer(prompt + completion, return_tensors="pt").to(device)
+    reward = model(**inputs).logits[0].item()
+    return reward
+```
+## Training Details
+The reward model was trained using:
+- Learning rate: 3 × 10⁻⁶
+- Gradient norm threshold: 1.0
+- Learning rate schedule: Linear
+- Batch size (effective): 256
+- Max token length: 2,048
+- Number of epochs: 1
+## 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},
+}
+```
+The training methodology follows the Tulu 3 paper:
+```
+@article{lambert2024tulu3,
+  title={TÜLU 3: Pushing Frontiers in Open Language Model Post-Training},
+  author={Lambert, Nathan and Morrison, Jacob and Pyatkin, Valentina and others},
+  year={2024},
+  journal={arXiv preprint arXiv:2411.15124}
+}
+```