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--- |
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language: |
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- en |
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tags: |
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- upstage |
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- llama |
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- instruct |
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- instruction |
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pipeline_tag: text-generation |
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--- |
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# LLaMa-65b-instruct model card |
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## Model Details |
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* **Developed by**: [Upstage](https://en.upstage.ai) |
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* **Backbone Model**: [LLaMA](https://github.com/facebookresearch/llama/tree/llama_v1) |
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* **Variations**: It has different model parameter sizes and sequence lengths: [30B/1024](https://huggingface.co/upstage/llama-30b-instruct), [30B/2048](https://huggingface.co/upstage/llama-30b-instruct-2048), [65B/1024](https://huggingface.co/upstage/llama-65b-instruct) |
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* **Language(s)**: English |
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) |
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* **License**: This model is under a **Non-commercial** Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format |
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* **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/llama-30b-instruct-2048/discussions) |
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* **Contact**: For questions and comments about the model, please email [[email protected]](mailto:[email protected]) |
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## Dataset Details |
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### Used Datasets |
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- Internal Orca-style dataset |
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> No other data was used except for the dataset mentioned above |
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### Prompt Template |
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``` |
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### System: |
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{System} |
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### User: |
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{User} |
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### Assistant: |
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{Assistant} |
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``` |
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## Hardware and Software |
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* **Hardware**: We utilized an A100x8 * 4 for training our model |
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* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer) |
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## Evaluation Results |
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### Overview |
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- We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). |
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We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`. |
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We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463). |
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### Main Results |
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| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | |
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|-----------------------------------------------|---------|-------|-----------|-------|------------| |
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| Llama-2-70b-instruct-v2 (Ours, Local Reproduction) | 72.7 | 71.6 | 87.7 | 69.7 | 61.6 | |
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| Llama-2-70b-instruct (Ours, Local Reproduction) | 72.0 | 70.7 | 87.4 | 69.3 | 60.7 | |
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| **llama-65b-instruct (Ours, Local Reproduction)** | **69.4** | **67.6** | **86.5** | **64.9** | **58.8** | |
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| Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 | |
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| llama-30b-instruct-2048 (Ours, Open LLM Leaderboard) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | |
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| llama-30b-instruct-2048 (Ours, Local Reproduction) | 67.0 | 64.9 | 85.0 | 61.9 | 56.0 | |
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| llama-30b-instruct (Ours, Open LLM Leaderboard) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | |
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| llama-65b | 64.2 | 63.5 | 86.1 | 63.9 | 43.4 | |
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| falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | |
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### Scripts |
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- Prepare evaluation environments: |
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``` |
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# clone the repository |
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git clone https://github.com/EleutherAI/lm-evaluation-harness.git |
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# check out the specific commit |
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git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 |
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# change to the repository directory |
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cd lm-evaluation-harness |
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``` |
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## Ethical Issues |
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### Ethical Considerations |
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- There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process. |
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## Contact Us |
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### Why Upstage LLM? |
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- [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. Our 30B model **outperforms all models around the world**, positioning itself as the leading performer. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm). |
