AELLM
/

Llama-3.2-Chibi-3B

@@ -1,200 +1,87 @@
 ---
 library_name: transformers
 tags:
-- llama-factory
 ---
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 ---
+license: llama3.2
+language:
+  - en
+  - ja
+  - de
+  - fr
+  - it
+  - pt
+  - hi
+  - es
+  - th
 library_name: transformers
+pipeline_tag: text-generation
+base_model: meta-llama/Llama-3.2-3B
+datasets:
+  - ryota39/izumi-lab-dpo-45k
+  - Aratako/Magpie-Tanuki-8B-97k
+  - kunishou/databricks-dolly-15k-ja
+  - kunishou/oasst1-89k-ja
 tags:
+  - llama3.2
 ---
+![chibi-img](./chibi.png)
+## Preface
+The importance of a small parameter large language model (LLM) lies in its ability to balance performance and efficiency. As LLMs grow increasingly sophisticated, the trade-off between model size and computational resource demands becomes critical. A smaller parameter model offers significant advantages, such as reduced memory usage, faster inference times, and lower energy consumption, all while retaining a high level of accuracy and contextual understanding. These models are particularly valuable in real-world applications where resources like processing power and storage are limited, such as on mobile devices, edge computing, or low-latency environments.
+## Llama 3.2 Chibi 3B
+This experimental model is the result from continual pre-training of [Meta's Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on a small mixture of japanese datasets.
+## Architecture
+[Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B)
+## Training
+The model has been trained with a following mixture of datasets:
+  - [ryota39/izumi-lab-dpo-45k](https://huggingface.co/ryota39/izumi-lab-dpo-45k)
+  - [Aratako/Magpie-Tanuki-8B-97k](https://huggingface.co/Aratako/Magpie-Tanuki-8B-97k)
+  - [kunishou/databricks-dolly-15k-ja](https://huggingface.co/kunishou/databricks-dolly-15k-ja)
+  - [kunishou/oasst1-89k-ja](https://huggingface.co/kunishou/oasst1-89k-ja)
+## Contributors
+- [Hammaam](https://huggingface.co/AELLM)
+## How to use
+Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+Make sure to update your transformers installation via pip install --upgrade transformers.
+```python
+import torch
+from transformers import pipeline
+model_id = "AELLM/Llama-3.2-Chibi-3B"
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+pipe("人生の鍵は")
+```
+# License
+Refer to [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+# References
+```bibtex
+@inproceedings{zheng2024llamafactory,
+  title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
+  author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
+  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
+  address={Bangkok, Thailand},
+  publisher={Association for Computational Linguistics},
+  year={2024},
+  url={http://arxiv.org/abs/2403.13372}
+}
+```