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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+
+
+<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 Card for DCLM-1B-IT
+
+DCLM-IT-1B is a 1.4B billion parameter language model trained on the DCLM-Baseline dataset and then further finetuned on our DCLM-IT finetuning mixture. 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 |
+|:------:|:-----------------:|:--------:|:-------------:|:-----------------:|:----------------:|
+| 1.4B   | 2.608T             | 24       | 2048          | 16                | 2048             |
+
+
+### Model Description
+
+- **Developed by:** DataComp for Language Models (DCLM) Team
+- **Model type:** Decoder-only Transformer language model
+- **Language(s):** English (primarily)
+- **License:** Apache 2.0
+- **Contact:** contact@datacomp.ai
+- **Date:** July 2024
+
+### Model Sources
+
+- **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)
+
+
+
+### Instruction Tuning Details
+
+The model was trained using the following setup:
+
+- **Architecture:** Decoder-only Transformer 
+- **Framework:** PyTorch with OpenLM
+- **Optimizer:** AdamW
+- **Learning Rate:** 2e-5 (peak)
+- **Weight Decay:** 0.1
+- **Batch Size:** 2048 sequences
+- **Sequence Length:** 2048 tokens
+- **Total Training Tokens:** 8.4B 
+- **Number of Epochs**: 10
+- **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-1B models on various tasks (using [llm-foundry](https://github.com/mosaicml/llm-foundry) eval suite)
+
+| Task      | Core   | Extended   | MMLU 5-shot   |
+|:---------:|:------:|:----------:|:-------------:|
+| DCLM-1B   | 42.3   | 25.1       | 41.9          |
+| DCLM-1B-v2| 45.2   | 28.1       | 47.5          |
+| DCLM-1B-IT| 47.1   | 33.6       | 51.4          |
+
+Note: All scores are presented as decimal values between 0 and 1, representing the proportion of correct answers or the model's performance on each task.
+
+Moreover, we present our evaluation results on Length-Controlled Alpaca-Eval 2.0 to measure our instruction-following capabilities.
+
+| Model                              | AlpacaEval2.0 LC Win-rate (%) |
+|------------------------------------|------------------------------:|
+| **Our runs**                       |                               |
+| DCLM-IT-1B                         |                8.6            |
+| DCLM-IT-7B                         |                16.6           |
+| Mistral-7B w/ OpenHermes 2.5       |                15.4           |
+| DCLM-Baseline-7B w/ OpenHermes 2.5 |                13.8           |
+| **Reported from the leaderboard**  |                               |
+| LLaMA-3-Instruct-8B                |                **22.9**       |
+| Mistral-v0.2-7B                    |                17.1           |
+| Mistral-7B w/ OpenHermes 2.5       |                16.2           |
+| Zephyr-Beta-7B                     |                13.2           |
+| Vicuna-v1.3-13B                    |                10.8           |
+| Gemma-Instruct-7B                  |                10.4           |
+| Nous-Hermes-13B                    |                9.7            |
+| DaVinci001                         |                9.0            |
+| LLaMA-2-Chat-13B                   |                8.4            |
+| Alpaca-7B                          |                5.9            |
+
+## Example Code
+
+This is example code on how to run the chat model.
+```
+ from transformers import AutoTokenizer
+    from open_lm.utils.transformers.hf_config import OpenLMConfig
+    import torch
+    from open_lm.utils.transformers.hf_model import OpenLMConfig, OpenLMforCausalLM
+
+    # Load the model and tokenizer
+    model_name = "TRI-ML/DCLM-1B-IT"
+    # Load the configuration, tokenizer, and model separately
+    config = OpenLMConfig.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = OpenLMforCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="cuda", config=config)
+
+    # Define the prompt format
+    def create_prompt(instruction):
+        PROMPT = '''Below is an instruction that describes a task.\n\nWrite a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:'''
+        return PROMPT.format(instruction=instruction)
+    # Example instruction
+    instruction = "Give me a poem about Sachin Tendulkar."
+    # Create the prompt
+    prompt = create_prompt(instruction)
+    # Tokenize the input
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch.device('cuda'))
+    # Generate the response
+    output = model.generate(input_ids, max_length=500, top_p=.95, do_sample=True, temperature=0.3)
+    # Decode the response
+    response = tokenizer.decode(output[0][len(input_ids[0]):])
+    response = response.split("<|endoftext|>")[0]
+    # Print the response
+    print(response)
+```
+
+## 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}
+}
+```