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--- |
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language: |
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- en |
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tags: |
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- llama2 |
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- llama-2 |
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- llama |
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- llama2 architecture |
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- litellama |
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datasets: |
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- Redpajama |
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metrics: |
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- MMLU |
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license: mit |
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--- |
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# LiteLlama: Reduced-Scale Llama |
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In this series of repos, we present an open-source reproduction of Meta AI's [LLaMa 2](https://ai.meta.com/llama/). However, with significantly reduced model sizes, [LiteLlama-460M-1T](https://huggingface.co/ahxt/LiteLlama-460M-1T) has 460M parameters trained with 1T tokens. |
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## Dataset and Tokenization |
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We train our models on part of [RedPajama](https://www.together.xyz/blog/redpajama) dataset. We use the [GPT2Tokenizer](https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/gpt2#transformers.GPT2Tokenizer) to tokenize the text. |
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## Training Details |
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The model was trained with ~1T tokens (0.98T). num of tokens = steps*length*batch_size=499679*1024*192=98240888832≈0.98T. |
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The training curve is at this [WandB project](https://wandb.ai/ahxt/llama2_xs_460M_training_loss/reports/reduced_train_loss-23-09-05-20-25-43---Vmlldzo1MzIwNDUx?accessToken=x2ch3n30jo77p1x8y7q9js4h4d8zpjtz1tzot4xxullyefixp4jwt7au2q37k2q6). |
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### Using with HuggingFace Transformers |
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The experimental checkpoints can be directly loaded by [Transformers](https://huggingface.co/transformers/) library. The following code snippet shows how to load the our experimental model and generate text with it. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_path = 'ahxt/LiteLlama-460M-1T' |
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model = AutoModelForCausalLM.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model.eval() |
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prompt = 'Q: What is the largest bird?\nA:' |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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tokens = model.generate(input_ids, max_length=20) |
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print( tokenizer.decode(tokens[0].tolist(), skip_special_tokens=True) ) |
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# Q: What is the largest bird?\nA: The largest bird is a black-headed gull. |
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``` |
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## Evaluation |
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### We evaluate our models on the MMLU task. |
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| Models | #parameters |zero-shot | 5-shot | |
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| --- | --- | --- | --- | |
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| llama | 7B | 28.46 | 35.05 | |
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| openllama | 3B | 24.90 | 26.71 | |
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|TinyLlama-1.1B-step-50K-105b | 1.1B | 19.00 | 26.53 | |
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| LiteLlama-460M-1T | 0.46B | 21.13 | 26.39 | |
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### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ahxt__llama2_xs_460M_experimental) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 26.65 | |
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| ARC (25-shot) | 24.91 | |
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| HellaSwag (10-shot) | 38.47 | |
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| MMLU (5-shot) | 26.17 | |
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| TruthfulQA (0-shot) | 41.59 | |
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| Winogrande (5-shot) | 49.88 | |
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| GSM8K (5-shot) | 0.0 | |
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| DROP (3-shot) | 5.51 | |
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## Contact |
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This model is developed by [Xiaotian Han](https://ahxt.github.io/) from Texas A&M University and released under MIT License. |
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