prince-canuma
/

Llama-3-6B-v0

@@ -1,199 +1,87 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+language:
+- en
+license: llama3
 library_name: transformers
+tags:
+- Llama-3-6B
+- 6B
 ---
+# Model Summary
+<img src="images/llama-3-6B icon.jpeg" width="500" alt="Llama-3-6B"/>
+Introducing the world's first Llama-3 base model with 6B parameters. This model is a untrained model which was created from Meta-Llama-3-8B using a technique called [downcycling](https://youtube.com/playlist?list=PLDn_JsyofyfTH5_5V1MNb8UYKxMl6IMNy&si=9hcOol4KHIgWThgt) .
+You can check trained version of this model here:
+https://huggingface.co/prince-canuma/Llama-3-6B-v0.1
 <!-- Provide a longer summary of what this model is. -->
+## Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [Prince Canuma](https://huggingface.co/prince-canuma)
+- **Sponsored by:** General
+- **Model type:** Llama
+- **License:** [Llama-3](https://llama.meta.com/llama3/license)
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/Blaizzy/Coding-LLMs-from-scratch/tree/main/Llama-3
+- **Video:** https://youtube.com/playlist?list=PLDn_JsyofyfTH5_5V1MNb8UYKxMl6IMNy&si=5Y4cm-6wrMOD1Abr
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+### **BibTeX:**
+```bibtex
+@misc{prince2024downcycling,
+      title={Efficient LLM Downcycling: Generating Diverse Model Sizes from Pretrained Giants},
+      author={Prince Canuma},
+      year={2024},
+}
+```
+# **Thank You!**
+I want to extend my heartfelt thanks to the community for the invaluable expertise and unwavering support.
+Additionally, I would like to thank Viet from General Catalyst (GC) for providing me with the much needed compute.
+This is my most ambitious project yet, and it wouldn't have been possible without the incredible open-source ML community!
+Developers, I am eager to see and hear about the innovative fine-tunes and applications you create.
+Users, I am excited to learn about your experiences and use cases.
+Thank you for your interest and support!
+## References:
+```bibtex
+@misc{komatsuzaki2023sparse,
+      title={Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints},
+      author={Aran Komatsuzaki and Joan Puigcerver and James Lee-Thorp and Carlos Riquelme Ruiz and Basil Mustafa and Joshua Ainslie and Yi Tay and Mostafa Dehghani and Neil Houlsby},
+      year={2023},
+      eprint={2212.05055},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+```bibtex
+@misc{sanyal2024pretraining,
+      title={Pre-training Small Base LMs with Fewer Tokens},
+      author={Sunny Sanyal and Sujay Sanghavi and Alexandros G. Dimakis},
+      year={2024},
+      eprint={2404.08634},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```