--- language: - en license: mit tags: - llama2 - llama-2 - llama - llama2 architecture - litellama datasets: - Redpajama metrics: - MMLU widget: - text: 'Q: What is the largest bird?\nA:' model-index: - name: LiteLlama-460M-1T results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 24.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ahxt/LiteLlama-460M-1T name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 38.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ahxt/LiteLlama-460M-1T name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.96 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ahxt/LiteLlama-460M-1T name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 41.59 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ahxt/LiteLlama-460M-1T name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 50.2 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ahxt/LiteLlama-460M-1T name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ahxt/LiteLlama-460M-1T name: Open LLM Leaderboard --- # LiteLlama: Reduced-Scale Llama 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 was developed by [Xiaotian Han](https://ahxt.github.io/) from Texas A&M University at the DATA Lab under the supervision of Prof. [Xia "Ben" Hu](https://cs.rice.edu/~xh37/index.html), and the model is released under MIT License. # [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__LiteLlama-460M-1T) | Metric |Value| |---------------------------------|----:| |Avg. |30.16| |AI2 Reasoning Challenge (25-Shot)|24.83| |HellaSwag (10-Shot) |38.39| |MMLU (5-Shot) |25.96| |TruthfulQA (0-shot) |41.59| |Winogrande (5-shot) |50.20| |GSM8k (5-shot) | 0.00|