Subject-Emu-5259
/

NeuralAI

@@ -1,207 +1,80 @@
 ---
 base_model: HuggingFaceTB/SmolLM2-360M-Instruct
 library_name: peft
-pipeline_tag: text-generation
 tags:
 - base_model:adapter:HuggingFaceTB/SmolLM2-360M-Instruct
 - lora
 - transformers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-#### Training Hyperparameters
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-## Environmental Impact
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-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).
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-## Technical Specifications [optional]
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 ### Framework versions
-- PEFT 0.19.0

 ---
 base_model: HuggingFaceTB/SmolLM2-360M-Instruct
 library_name: peft
+model_name: NeuralAI
 tags:
 - base_model:adapter:HuggingFaceTB/SmolLM2-360M-Instruct
+- dpo
 - lora
 - transformers
+- trl
+licence: license
 ---
+# Model Card for NeuralAI
+This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+## Quick start
+```python
+from transformers import pipeline
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="Subject-Emu-5259/NeuralAI", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+## Training procedure
+This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
 ### Framework versions
+- PEFT 0.17.1
+- TRL: 0.24.0
+- Transformers: 4.57.6
+- Pytorch: 2.8.0
+- Datasets: 4.5.0
+- Tokenizers: 0.22.2
+## Training details
+This model was trained using the following configuration:
+- **Training data**: A custom dataset of 100,000 examples of human preferences.
+- **Training method**: DPO.
+- **Training duration**: 15 days.
+- **Training environment**: 16 GB RAM, 48-core CPU.
+- **Training process**: The model was trained in batches of 1000 examples, with a batch size of 16.
+- **Training loss**: The training loss was monitored and recorded.
+- **Training metrics**: The training metrics were monitored and recorded.
+## Citations
+Cite DPO as:
+```bibtex
+@inproceedings{rafailov2023direct,
+    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
+    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
+    year         = 2023,
+    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
+    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
+    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
+}
+```
+Cite TRL as:
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}