Update README.md

README.md

@@ -1,199 +1,156 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- tiiuae/falcon-refinedweb
+- bigcode/starcoderdata
+- togethercomputer/RedPajama-Data-1T
 ---
 
-# Model Card for Model ID
+# OpenLLaMA: An Open Reproduction of LLaMA
 
-<!-- Provide a quick summary of what the model is/does. -->
+**TL;DR**: we are releasing our public preview of OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA. We are releasing a series of 3B, 7B and 13B models trained on different data mixtures. Our model weights can serve as the drop in replacement of LLaMA in existing implementations.
 
+In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a series of 3B, 7B and 13B models trained on 1T tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. The v2 model is better than the old v1 model trained on a different data mixture. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details.
 
 
-## Model Details
+## Weights Release, License and Usage
 
-### Model Description
+We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license.
 
-<!-- Provide a longer summary of what this model is. -->
+### Loading the Weights with Hugging Face Transformers
+Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that** [**the auto-converted fast tokenizer sometimes gives incorrect tokenizations**](https://github.com/huggingface/transformers/issues/24233)**.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+```python
+import torch
+from transformers import LlamaTokenizer, LlamaForCausalLM
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## v2 models
+model_path = 'openlm-research/open_llama_3b_v2'
+# model_path = 'openlm-research/open_llama_7b_v2'
 
-### Model Sources [optional]
+## v1 models
+# model_path = 'openlm-research/open_llama_3b'
+# model_path = 'openlm-research/open_llama_7b'
+# model_path = 'openlm-research/open_llama_13b'
 
-<!-- Provide the basic links for the model. -->
+tokenizer = LlamaTokenizer.from_pretrained(model_path)
+model = LlamaForCausalLM.from_pretrained(
+    model_path, torch_dtype=torch.float16, device_map='auto',
+)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+prompt = 'Q: What is the largest animal?\nA:'
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
 
-## Uses
+generation_output = model.generate(
+    input_ids=input_ids, max_new_tokens=32
+)
+print(tokenizer.decode(generation_output[0]))
+```
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama).
 
-### Direct Use
+### Evaluating with LM-Eval-Harness
+The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```python
+tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained(
+    pretrained if tokenizer is None else tokenizer,
+    revision=revision + ("/" + subfolder if subfolder is not None else ""),
+    use_fast=False
+)
+```
 
-[More Information Needed]
+### Loading the Weights with EasyLM
+For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights.
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+## Dataset and Training
 
-### Out-of-Scope Use
+The v1 models are trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). The v2 models are trained on a mixture of the [Falcon refined-web dataset](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), the [StarCoder dataset](https://huggingface.co/datasets/bigcode/starcoderdata) and the wikipedia, arxiv, book and stackexchange part of the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).  We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer.  The only difference between our setting and the original one is the dataset used: OpenLLaMA employs open datasets rather than the one utilized by the original LLaMA.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and fully sharded data parallelism [](https://engineering.fb.com/2021/07/15/open-source/fsdp/)(also know as ZeRO stage 3) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model.
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).  The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
+
+The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens.  We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
+
+
+| **Task/Metric**        | GPT-J 6B | LLaMA 7B | LLaMA 13B | OpenLLaMA 3Bv2 | OpenLLaMA 7Bv2 | OpenLLaMA 3B | OpenLLaMA 7B | OpenLLaMA 13B |
+| ---------------------- | -------- | -------- | --------- | -------------- | -------------- | ------------ | ------------ | ------------- |
+| anli_r1/acc            | 0.32     | 0.35     | 0.35      | 0.33           | 0.34           | 0.33         | 0.33         | 0.33          |
+| anli_r2/acc            | 0.34     | 0.34     | 0.36      | 0.36           | 0.35           | 0.32         | 0.36         | 0.33          |
+| anli_r3/acc            | 0.35     | 0.37     | 0.39      | 0.38           | 0.39           | 0.35         | 0.38         | 0.40          |
+| arc_challenge/acc      | 0.34     | 0.39     | 0.44      | 0.34           | 0.39           | 0.34         | 0.37         | 0.41          |
+| arc_challenge/acc_norm | 0.37     | 0.41     | 0.44      | 0.36           | 0.41           | 0.37         | 0.38         | 0.44          |
+| arc_easy/acc           | 0.67     | 0.68     | 0.75      | 0.68           | 0.73           | 0.69         | 0.72         | 0.75          |
+| arc_easy/acc_norm      | 0.62     | 0.52     | 0.59      | 0.63           | 0.70           | 0.65         | 0.68         | 0.70          |
+| boolq/acc              | 0.66     | 0.75     | 0.71      | 0.66           | 0.72           | 0.68         | 0.71         | 0.75          |
+| hellaswag/acc          | 0.50     | 0.56     | 0.59      | 0.52           | 0.56           | 0.49         | 0.53         | 0.56          |
+| hellaswag/acc_norm     | 0.66     | 0.73     | 0.76      | 0.70           | 0.75           | 0.67         | 0.72         | 0.76          |
+| openbookqa/acc         | 0.29     | 0.29     | 0.31      | 0.26           | 0.30           | 0.27         | 0.30         | 0.31          |
+| openbookqa/acc_norm    | 0.38     | 0.41     | 0.42      | 0.38           | 0.41           | 0.40         | 0.40         | 0.43          |
+| piqa/acc               | 0.75     | 0.78     | 0.79      | 0.77           | 0.79           | 0.75         | 0.76         | 0.77          |
+| piqa/acc_norm          | 0.76     | 0.78     | 0.79      | 0.78           | 0.80           | 0.76         | 0.77         | 0.79          |
+| record/em              | 0.88     | 0.91     | 0.92      | 0.87           | 0.89           | 0.88         | 0.89         | 0.91          |
+| record/f1              | 0.89     | 0.91     | 0.92      | 0.88           | 0.89           | 0.89         | 0.90         | 0.91          |
+| rte/acc                | 0.54     | 0.56     | 0.69      | 0.55           | 0.57           | 0.58         | 0.60         | 0.64          |
+| truthfulqa_mc/mc1      | 0.20     | 0.21     | 0.25      | 0.22           | 0.23           | 0.22         | 0.23         | 0.25          |
+| truthfulqa_mc/mc2      | 0.36     | 0.34     | 0.40      | 0.35           | 0.35           | 0.35         | 0.35         | 0.38          |
+| wic/acc                | 0.50     | 0.50     | 0.50      | 0.50           | 0.50           | 0.48         | 0.51         | 0.47          |
+| winogrande/acc         | 0.64     | 0.68     | 0.70      | 0.63           | 0.66           | 0.62         | 0.67         | 0.70          |
+| Average                | 0.52     | 0.55     | 0.57      | 0.53           | 0.56           | 0.53         | 0.55         | 0.57          |
+
+
+We removed the task CB and WSC from our benchmark, as our model performs suspiciously high on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
+
+
+## Contact
+
+We would love to get feedback from the community. If you have any questions, please open an issue or contact us.
+
+OpenLLaMA is developed by:
+[Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research.
+*Equal Contribution
+
+
+
+## Acknowledgment
+
+We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback.
+
+The OpenLLaMA 13B v1 model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support.
+
+
+## Reference
+
+If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
+```
+@software{openlm2023openllama,
+  author = {Geng, Xinyang and Liu, Hao},
+  title = {OpenLLaMA: An Open Reproduction of LLaMA},
+  month = May,
+  year = 2023,
+  url = {https://github.com/openlm-research/open_llama}
+}
+```
+```
+@software{together2023redpajama,
+  author = {Together Computer},
+  title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
+  month = April,
+  year = 2023,
+  url = {https://github.com/togethercomputer/RedPajama-Data}
+}
+```
+```
+@article{touvron2023llama,
+  title={Llama: Open and efficient foundation language models},
+  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
+  journal={arXiv preprint arXiv:2302.13971},
+  year={2023}
+}
+```