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
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. However, with significantly reduced model sizes, LiteLlama-460M-1T has 460M parameters trained with 1T tokens.
Dataset and Tokenization
We train our models on part of RedPajama dataset. We use the GPT2Tokenizer to tokenize the text.
Training Details
The model was trained with ~1T tokens (0.98T). num of tokens = stepslengthbatch_size=4996791024192=98240888832≈0.98T.
The training curve is at this WandB project.
Using with HuggingFace Transformers
The experimental checkpoints can be directly loaded by Transformers library. The following code snippet shows how to load the our experimental model and generate text with it.
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
Detailed results can be found here
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 from Texas A&M University at the DATA Lab under the supervision of Prof. Xia "Ben" Hu, and the model is released under MIT License.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
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 |