Create README.md

README.md

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+---
+base_model:
+- HuggingFaceTB/SmolLM2-1.7B-Instruct
+datasets:
+- jjzha/fs1-2708
+language:
+- en
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+tags:
+- en
+- factuality
+- thinking
+- reasoning
+---
+
+## Model Details
+
+**SmolLM2-1.7B-Instruct-rt-2708** is a 1.7B parameter language model designed for English text generation tasks. This model builds upon [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) and is further fine-tuned on the [jjzha/fs1-2708](https://huggingface.co/datasets/jjzha/fs1-2708) dataset. It focuses on enhancing factual reasoning abilities in generated text.
+
+### Model Developers
+
+This model was fine-tuned by independent contributors using the Hugging Face Transformers library.
+
+### Variations
+
+This is a fine-tuned version of the `HuggingFaceTB/SmolLM2-1.7B-Instruct` model. No additional variants or intermediate checkpoints are currently provided.
+
+### Input
+
+Text only.
+
+### Output
+
+Text only.
+
+### Model Architecture
+
+The model is an auto-regressive, transformer-based language model, fine-tuned with supervised learning to improve instruction-following and reasoning capabilities in English.
+
+### Model Dates
+
+Fine-tuning was performed in February-April 2025. The base and instruct model was originally released by the Qwen team.
+
+### License
+
+This model is released under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
+
+### Research Paper
+
+[Scaling Reasoning can Improve Factuality in Large Language Models](https://huggingface.co/papers/2505.11140)
+
+## Intended Use & Limitations
+
+### Intended Use Cases
+
+This model is intended for English language text generation tasks that require improved factual accuracy and reasoning. It is suitable for research, experimentation, and development of assistant-like chat applications.
+
+The instruction-tuned base model follows the Qwen instruction format, and this fine-tuned version preserves that behavior.
+
+### Limitations
+
+Despite improvements, the model may still produce factually incorrect or logically inconsistent outputs. It is not recommended for high-stakes decision-making applications without human oversight. Always verify generated content before relying on it in critical scenarios.
+
+## Hardware and Software
+
+### Training Factors
+
+Fine-tuning was performed using the Hugging Face Transformers library and Pytorch FSDP. We used a multinode and multigpu setup with AMD MI250x GPUs.
+
+### Carbon Footprint
+
+We only have aggregated statistics of all models fine-tuned and inferences. A cumulative of 6,500 GPU hours of computation was performed on AMD MI250x GPU
+modules, which has a TDP of 500 Watts. The experiments were ran from February to April 2025. During this time, the average carbon efficiency in Finland was 0.085 kg/kW h.
+This means we released about 276 kg of CO2 equivalent.
+
+## Training Data
+
+### Overview
+
+Fine-tuning was performed on the [jjzha/fs1-2708](https://huggingface.co/datasets/jjzha/fs1-2708) dataset, which focuses on enhancing reasoning and factual accuracy.
+
+## Evaluation Results
+
+See paper for results.
+
+## Citation
+
+```
+@misc{zhang2025scalingreasoningimprovefactuality,
+      title={Scaling Reasoning can Improve Factuality in Large Language Models}, 
+      author={Mike Zhang and Johannes Bjerva and Russa Biswas},
+      year={2025},
+      eprint={2505.11140},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.11140}, 
+}
+```
+
+Code: https://github.com/jjzha/fs1