SultanR
/

SmolTulu-1.7b-Reinforced

@@ -1,199 +1,76 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+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 Banner](smoltulubanner.png)
+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://allenai.org/blog/tulu-3-technical)
+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},
+}
+```