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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-2 |
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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-2-70b-instruct-1024 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-2](https://github.com/facebookresearch/llama/tree/main) |
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* **Language(s)**: English |
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) |
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* **License**: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) |
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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-2-70b-instruct-v2/discussions) |
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* **Contact**: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai) |
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## Dataset Details |
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### Used Datasets |
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- Orca-style dataset |
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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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## Usage |
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- Tested on A100 80GB |
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- Our model can handle up to 10k input tokens, thanks to the `rope_scaling` option |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2") |
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model = AutoModelForCausalLM.from_pretrained( |
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"upstage/Llama-2-70b-instruct-v2", |
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device_map="auto", |
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torch_dtype=torch.float16, |
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load_in_8bit=True, |
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rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs |
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) |
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prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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del inputs["token_type_ids"] |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf')) |
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output_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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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) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index) |
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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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- We used [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge), a set of challenging multi-turn open-ended questions, to evaluate the models |
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### Main Results |
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| Model | H4(Avg) | ARC | HellaSwag | MMLU | TruthfulQA | | MT_Bench | |
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|--------------------------------------------------------------------|----------|----------|----------|------|----------|-|-------------| |
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| **[Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)**(***Ours***, ***Local Reproduction***) | **72.7** | **71.6** | **87.7** | 69.7 | **61.6** | | **7.44063** | |
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| [Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) (Ours, Open LLM Leaderboard) | 72.3 | 70.9 | 87.5 | **69.8** | 61 | | 7.24375 | |
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| [llama-65b-instruct](https://huggingface.co/upstage/llama-65b-instruct) (Ours, Open LLM Leaderboard) | 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](https://huggingface.co/upstage/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](https://huggingface.co/upstage/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 for H4 Score Reproduction |
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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 70B 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) |