--- language: - tr tags: - llama2 - llama-2 - llama - llama2 architecture - litellama datasets: - Redpajama metrics: - MMLU license: mit widget: - text: "Q: What is the largest bird?\\nA:" --- # LiteLlama: Reduced-Scale Llama 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. ## Dataset and Tokenization 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. ## Training Details The model was trained with ~1T tokens (0.98T). num of tokens = steps*length*batch_size=499679*1024*192=98240888832≈0.98T. 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). ### Using with HuggingFace Transformers 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. ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path = 'ahxt/LiteLlama-460M-1T' model = AutoModelForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) model.eval() prompt = 'Q: What is the largest bird?\nA:' input_ids = tokenizer(prompt, return_tensors="pt").input_ids tokens = model.generate(input_ids, max_length=20) print( tokenizer.decode(tokens[0].tolist(), skip_special_tokens=True) ) # Q: What is the largest bird?\nA: The largest bird is a black-headed gull. ``` ## Evaluation ### We evaluate our models on the MMLU task. | Models | #parameters |zero-shot | 5-shot | | --- | --- | --- | --- | | llama | 7B | 28.46 | 35.05 | | openllama | 3B | 24.90 | 26.71 | |TinyLlama-1.1B-step-50K-105b | 1.1B | 19.00 | 26.53 | | LiteLlama-460M-1T | 0.46B | 21.13 | 26.39 | ### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ahxt__llama2_xs_460M_experimental) | Metric | Value | |-----------------------|---------------------------| | Avg. | 26.65 | | ARC (25-shot) | 24.91 | | HellaSwag (10-shot) | 38.47 | | MMLU (5-shot) | 26.17 | | TruthfulQA (0-shot) | 41.59 | | Winogrande (5-shot) | 49.88 | | GSM8K (5-shot) | 0.0 | | DROP (3-shot) | 5.51 | ## Contact This model is developed by [Xiaotian Han](https://ahxt.github.io/) from Texas A&M University and released under MIT License.