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README.md

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-license: apple-ascl
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+---
+license: apple-ascl
+---
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63118add64939fabc0108b28/BB42g4V8HTxb5dR4tcy8A.png)
+
+# Model Card for DCLM-Baseline-7B
+
+DCLM-Baseline-7B is a 7 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.
+
+## Model Details
+
+| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
+|------|-----------------|--------|-------------|-----------------|----------------|
+| 7B   | 2.5T            | 32     | 4096        | 32              | 2048           |
+
+### Model Description
+
+- **Developed by:** DataComp for Language Models (DCLM) Team
+- **Model type:** Decoder-only Transformer language model
+- **Language(s):** English (primarily)
+- **License:** Apple Sample Code License
+- **Contact:** [email protected]
+- **Date:** June 2024
+
+### Model Sources
+
+- **Repository:** https://github.com/datacomp-team/dclm
+- **Paper:** [DataComp-LM: In search of the next generation of training sets for language models](https://arxiv.org/abs/2406.11794)
+
+## Uses
+
+### Inference
+
+To use the model for inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("datacomp/dclm-baseline-7b")
+tokenizer = AutoTokenizer.from_pretrained("datacomp/dclm-baseline-7b")
+
+prompt = "Language modeling is"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Training Details
+
+The model was trained using the following setup:
+
+- **Architecture:** Decoder-only Transformer
+- **Framework:** PyTorch with OpenLM
+- **Optimizer:** AdamW
+- **Learning Rate:** 2e-3 (peak)
+- **Weight Decay:** 0.05
+- **Batch Size:** 2048 sequences
+- **Sequence Length:** 2048 tokens
+- **Total Training Tokens:** 2.6T
+- **Hardware:** Trained on H100 GPUs
+
+For more detailed training information, please refer to Section 3.4 and Appendix F of the DCLM paper.
+
+## Evaluation
+
+Here are the evaluation results for DCLM-Baseline-7B on various tasks:
+
+| Task                     | Score   |
+|--------------------------|---------|
+| CORE                     | 57.1    |
+| MMLU (5-shot)            | 63.7    |
+| EXTENDED                 | 45.4    |
+| ARC Challenge            | 57.68   |
+| ARC Easy                 | 81.82   |
+| BoolQ                    | 83.36   |
+| COPA                     | 87.00   |
+| HellaSwag                | 80.68   |
+| OpenBookQA               | 46.40   |
+| PIQA                     | 80.85   |
+| Winogrande               | 73.80   |
+| AGI Eval LSAT AR (3-shot)| 29.57   |
+| GSM8K (CoT)              | 17.13   |
+
+For a complete list of evaluation results, please refer to the full evaluation JSON file.
+
+## Limitations and Biases
+
+While DCLM-Baseline-7B 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}
+}
+```