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README.md
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# Model Card for
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## Model
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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### Downstream Use [optional]
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### Out-of-Scope Use
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<!--
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##
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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tags: []
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---
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# Model Card for LION-Gemma-2b-sft-v1.0
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The LION-series are trained using an **empirically optimized pipeline** that consists of three stages: SFT, DPO, and online preference learning (online DPO). We find simple techniques such as sequence packing, loss masking in SFT, increasing the preference dataset size in DPO, and online DPO training can significantly improve the performance of language models. Our best models (in the LION-series) **exceed the performance of the official instruct models** tuned with closed-source data and algorithms.
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For training datasets, code, and evaluation scripts, please refer to our paper and codebase (to-be-released).
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## Model description
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This model is finetuned from [`gemma-2b`](https://huggingface.co/google/gemma-2b) using SFT from the LION pipeline.
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- **Model type:** [`gemma-2b`](https://huggingface.co/google/gemma-2b)
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- **Language(s) (NLP):** Primarily English
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- **License:** Gemma Terms of Use
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- **Finetuned from model:** [`gemma-2b`](https://huggingface.co/google/gemma-2b)
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## Performance
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| Model | Method | Size | Arena-Hard | AlpacaEval-2 | MT-Bench | OpenLLM |
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|-------------|--------|------|------:|------:|---------:|-------:|
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|[Gemma-2b](https://huggingface.co/google/gemma-2b) | - | 2B | - | - | - | 46.69 |
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|[Gemma-2b-it](https://huggingface.co/google/gemma-2b-it) | SFT+RLHF | 2B | 3.4 | 5.44 | 5.63 | 42.75 |
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|[Gemma-2b-zephyr](https://huggingface.co/wandb/gemma-2b-zephyr-dpo) | SFT+DPO | 2B | 0.9 | 2.65 | 4.13 | 46.92 |
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|[LLaMA-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) | SFT | 7B | 4.6 | 5.35 | 6.22 | 53.16 |
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|[Vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) | SFT | 7B | 2.5 | 7.62 | 6.57 | 52.06 |
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|⮕ [LION-Gemma-2b-sft-v1.0 (ours)](https://huggingface.co/Columbia-NLP/LION-Gemma-2b-sft-v1.0) | SFT | 2B | 2.4 | 7.79 | 6.37 | 54.78 |
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|[LION-Gemma-2b-dpo-v1.0 (ours)](https://huggingface.co/Columbia-NLP/LION-Gemma-2b-dpo-v1.0) | SFT+DPO | 2B | 4.6 | 8.75 | 6.58 | 55.35 |
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|[LION-Gemma-2b-odpo-v1.0 (ours)](https://huggingface.co/Columbia-NLP/LION-Gemma-2b-odpo-v1.0) | SFT+DPO+ODPO | 2B | 5.0 | 9.57 | 6.75 | 55.98 |
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## Intended uses
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To ensure reproducibility, please use the following chat templates:
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```python
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import torch
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="Columbia-NLP/LION-Gemma-2b-sft-v1.0",
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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messages = [
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{
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"role": "system",
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"content": "",
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},
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{
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"role": "user",
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"content": "Write a short paragraph where every sentence start with the letter A."
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},
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]
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outputs = pipe(
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messages,
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max_new_tokens=128,
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do_sample=True,
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temperature=0.7,
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top_p=0.7,
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stop_sequence="<|im_end|>",
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)
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print(outputs[0]["generated_text"][-1]["content"])
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# Alice always admired the beautiful, amber leaves of the aspen trees.
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# Astonishingly, she noticed that the leaves would occasionally fall gently to the ground, creating a serene atmosphere.
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# Admiring the autumn scenery, Alice appreciated the artistry of nature.
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```
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to inspect the chat template/manually do generation:
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```python
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tokenizer = AutoTokenizer.from_pretrained("Columbia-NLP/LION-Gemma-2b-sft-v1.0")
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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print(prompt)
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# tokenize prompt and use model.generate
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```
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### Training details
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Please refer to our codebase at (to-be-released).
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<!-- ## Citation Information
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If you find this model useful in your work, please consider citing our paper:
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```
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@misc{tmp}
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``` -->
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## Acknowledgements
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We thank the Columbia-NLP group and [articulate.ai](https://www.articulateai.com/) for providing OpenAI API credits and computational resources to conduct our experiments.
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