Update README

README.md

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-library_name: transformers
-tags: []
+license: apache-2.0
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-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
+<img src="https://cdn-uploads.huggingface.co/production/uploads/63118add64939fabc0108b28/BB42g4V8HTxb5dR4tcy8A.png" alt="DCLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### 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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+# Model Card for DCLM-1B
 
-[More Information Needed]
+DCLM-1B is a 1.4 billion parameter language model trained on the DCLM-Baseline dataset, which was curated as part of the DataComp for Language Models (DCLM) benchmark. This model is designed to showcase the effectiveness of systematic data curation techniques for improving language model performance.
 
-## 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
+## Model Details
 
-<!-- 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. -->
+| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
+|:------:|:-----------------:|:--------:|:-------------:|:-----------------:|:----------------:|
+| 1.4B   | 4.3T            | 24     | 2048        | 16              | 2048           |
 
-[More Information Needed]
 
-### Training Procedure
+### Model Description
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+- **Developed by:** DataComp for Language Models (DCLM) Team
+- **Model type:** Decoder-only Transformer language model
+- **Language(s):** English (primarily)
+- **License:** Apache 2.0
+- **Contact:** [email protected]
+- **Date:** July 2024
 
-#### Preprocessing [optional]
+### Model Sources
 
-[More Information Needed]
+- **Repository:** https://github.com/mlfoundations/dclm
+- **Dataset:** https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0
+- **Paper:** [DataComp-LM: In search of the next generation of training sets for language models](https://arxiv.org/abs/2406.11794)
 
 
-#### Training Hyperparameters
+### Training Details
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+The model was trained using the following setup:
 
-#### Speeds, Sizes, Times [optional]
+- **Architecture:** Decoder-only Transformer 
+- **Framework:** PyTorch with OpenLM
+- **Optimizer:** AdamW
+- **Learning Rate:** 1e-2 (peak)
+- **Weight Decay:** 1e-2
+- **Batch Size:** 2048 sequences
+- **Sequence Length:** 2048 tokens
+- **Total Training Tokens:** 4.3T
+- **Hardware:** Trained on H100 GPUs
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+We train our 1.4B model for 4.3T tokens on DCLM-Baseline, combined with the
+StarCoder and ProofPile2 datasets.
+We will update our paper soon with more training details.
 
 ## 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
+Here are the evaluation results for DCLM-1B on various tasks (using [llm-foundry](https://github.com/mosaicml/llm-foundry) eval suite), compared to recently released small models on key benchmarks.
+As described in the paper, Core accuracy is the average of centered accuracy on
+22 tasks (including HellaSwag and ARC-E), Extended is centered accuracy averaged
+over 53 tasks.  We evaluate the models using llm-foundry.
+
+
+| Model                             | Params | Tokens | Open dataset? | \lowvar  | MMLU     | \aggscore |
+|-----------------------------------|--------|--------|---------------|----------|----------|-----------|
+| **Open weights, closed datasets** |        |        |               |          |          |           |
+| Qwen2-1.5B                        | 1.5B   | ?      | ❌             | 42.1     | **56.4** | **32.4**  |
+| Gemma-2B                          | 2.5B   | 3T     | ❌             | **43.3** | 40.8     | 26.6      |
+| **Open weights, open datasets**   |        |        |               |          |          |           |
+| OLMo-1B                           | 1.2B   | 3T     | ✅             | 29.7     | 26.0     | 16.1      |
+| SmolLM                            | 1.7B   | 1T     | ✅             | 36.3     | 30.0     | 21.2      |
+| DCLM-1B                           | 1.4B   | 4.3T   | ✅             | **45.2** | **47.5** | **28.1**  |
+
+| Task                                     | Score   |
+|------------------------------------------|---------|
+| AGI Eval LSAT AR                         |  0.2652 |
+| AGI Eval LSAT LR                         |  0.3314 |
+| AGI Eval LSAT RC                         |  0.4179 |
+| AGI Eval SAT English                     |  0.4709 |
+| AGI Eval SAT Math (CoT)                  |  0.0318 |
+| AQuA (CoT)                               |  0.0245 |
+| ARC (challenge)                          |  0.4744 |
+| ARC (easy)                               |  0.7462 |
+| BBQ                                      |  0.5151 |
+| BigBench Conceptual Combinations         |  0.5437 |
+| BigBench Conlang Translation             |  0.0793 |
+| BigBench CS Algorithms                   |  0.4720 |
+| BigBench Dyck Languages                  |  0.2210 |
+| BigBench Elementary Math QA              |  0.2598 |
+| BigBench Language Identification         |  0.3284 |
+| BigBench Logical Deduction               |  0.2473 |
+| BigBench Misconceptions                  |  0.5662 |
+| BigBench Novel Concepts                  |  0.5000 |
+| BigBench Operators                       |  0.3476 |
+| BigBench QA Wikidata                     |  0.6852 |
+| BigBench Repeat Copy Logic               |  0.1250 |
+| BigBench Strange Stories                 |  0.6724 |
+| BigBench Strategy QA                     |  0.5671 |
+| BigBench Understanding Fables            |  0.4603 |
+| BoolQ                                    |  0.7382 |
+| CommonSenseQA                            |  0.6708 |
+| COPA                                     |  0.8200 |
+| CoQA                                     |  0.4314 |
+| Enterprise PII Classification            |  0.5246 |
+| GPQA Diamond                             |  0.2424 |
+| GPQA                                     |  0.2500 |
+| GSM8K (CoT)                              |  0.0629 |
+| HellaSwag                                |  0.7285 |
+| HellaSwag (zero-shot)                    |  0.7162 |
+| Jeopardy                                 |  0.4514 |
+| LAMBADA (OpenAI)                         |  0.6992 |
+| LogiQA                                   |  0.3103 |
+| MathQA                                   |  0.2682 |
+| MMLU (few-shot)                          |  0.4752 |
+| MMLU (zero-shot)                         |  0.4175 |
+| OpenBookQA                               |  0.4280 |
+| PIQA                                     |  0.7829 |
+| PubMedQA (labeled)                       |  0.3790 |
+| Simple Arithmetic (no spaces)            |  0.0650 |
+| Simple Arithmetic (with spaces)          |  0.0700 |
+| SIQA                                     |  0.6868 |
+| SQuAD                                    |  0.5494 |
+| SVAMP (CoT)                              |  0.2733 |
+| TriviaQA (small subset)                  |  0.4133 |
+| Winogender (MC female)                   |  0.4667 |
+| Winogender (MC male)                     |  0.4000 |
+| Winograd                                 |  0.8608 |
+| Winogrande                               |  0.6630 |
+
+
+## Limitations and Biases
+
+While DCLM-1B demonstrates strong performance across a range of tasks, it's important to note:
+
+1. The model may exhibit biases present in its training data, which is derived from web crawl data.
+2. It has not undergone specific alignment or safety fine-tuning, so outputs should be used with caution.
+3. Performance on tasks not included in the evaluation suite may vary.
+4. The model's knowledge is limited to its training data cutoff date.
+
+## Ethical Considerations
+
+Users should be aware that this model, like all large language models, can potentially generate harmful or biased content. It should not be used for making decisions about individuals or in sensitive applications without appropriate safeguards and human oversight.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@article{Li2024DataCompLM,
+  title={DataComp-LM: In search of the next generation of training sets for language models},
+  author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and [... full author list]},
+  journal={arXiv preprint arXiv:2406.11794},
+  year={2024}
+}
+```
 
-[More Information Needed]