313 lines
13 KiB
Markdown
313 lines
13 KiB
Markdown
---
|
||
language:
|
||
- en
|
||
license: apache-2.0
|
||
library_name: transformers
|
||
datasets:
|
||
- cerebras/SlimPajama-627B
|
||
metrics:
|
||
- accuracy
|
||
model-index:
|
||
- name: MicroLlama
|
||
results:
|
||
- task:
|
||
type: text-generation
|
||
name: Text Generation
|
||
dataset:
|
||
name: IFEval (0-Shot)
|
||
type: HuggingFaceH4/ifeval
|
||
args:
|
||
num_few_shot: 0
|
||
metrics:
|
||
- type: inst_level_strict_acc and prompt_level_strict_acc
|
||
value: 19.85
|
||
name: strict accuracy
|
||
source:
|
||
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama
|
||
name: Open LLM Leaderboard
|
||
- task:
|
||
type: text-generation
|
||
name: Text Generation
|
||
dataset:
|
||
name: BBH (3-Shot)
|
||
type: BBH
|
||
args:
|
||
num_few_shot: 3
|
||
metrics:
|
||
- type: acc_norm
|
||
value: 2.83
|
||
name: normalized accuracy
|
||
source:
|
||
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama
|
||
name: Open LLM Leaderboard
|
||
- task:
|
||
type: text-generation
|
||
name: Text Generation
|
||
dataset:
|
||
name: MATH Lvl 5 (4-Shot)
|
||
type: hendrycks/competition_math
|
||
args:
|
||
num_few_shot: 4
|
||
metrics:
|
||
- type: exact_match
|
||
value: 0.0
|
||
name: exact match
|
||
source:
|
||
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama
|
||
name: Open LLM Leaderboard
|
||
- task:
|
||
type: text-generation
|
||
name: Text Generation
|
||
dataset:
|
||
name: GPQA (0-shot)
|
||
type: Idavidrein/gpqa
|
||
args:
|
||
num_few_shot: 0
|
||
metrics:
|
||
- type: acc_norm
|
||
value: 1.45
|
||
name: acc_norm
|
||
source:
|
||
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama
|
||
name: Open LLM Leaderboard
|
||
- task:
|
||
type: text-generation
|
||
name: Text Generation
|
||
dataset:
|
||
name: MuSR (0-shot)
|
||
type: TAUR-Lab/MuSR
|
||
args:
|
||
num_few_shot: 0
|
||
metrics:
|
||
- type: acc_norm
|
||
value: 4.79
|
||
name: acc_norm
|
||
source:
|
||
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama
|
||
name: Open LLM Leaderboard
|
||
- task:
|
||
type: text-generation
|
||
name: Text Generation
|
||
dataset:
|
||
name: MMLU-PRO (5-shot)
|
||
type: TIGER-Lab/MMLU-Pro
|
||
config: main
|
||
split: test
|
||
args:
|
||
num_few_shot: 5
|
||
metrics:
|
||
- type: acc
|
||
value: 1.53
|
||
name: accuracy
|
||
source:
|
||
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama
|
||
name: Open LLM Leaderboard
|
||
---
|
||
|
||
# Model Card for Model ID
|
||
|
||
As an individual with limited access and compute, I have been wondering if I could build a decent large-language model for a while. As the big mega corporations are focused on getting bigger and bigger models, I am going small!
|
||
|
||
As a result, I set up the following goals to **pretraining** a **300M Llama model** with the following restrictions:
|
||
|
||
1. My overall budget is $500.
|
||
2. Must pretrain an LLM from scratch with a fully open-source dataset and model.
|
||
3. Not allowed to finetune a model or use another LLM such as GPT-4 to generate any training data.
|
||
|
||
|
||
## Model Details
|
||
|
||
This project is heavily based on [TinyLlama](https://github.com/jzhang38/TinyLlama), which is an awesome open-source project aimed to **pretraining** a **1.1.1B Llama model on 1T tokens**.
|
||
|
||
This project is work in progress. Currently, I have spent \$280 on compute using 4 x Nvidia 4090 on [Vast.ai](https://vast.ai) and \$3 on AWS S3 storage after 4 days of training of the **300M Llama model** with **50B** tokens.
|
||
|
||
I modified [TinyLlama](https://github.com/jzhang38/TinyLlama) to support the following features (I will release my forked version of the source code after some clean up):
|
||
1. Pretrain a smaller size 300M model on [Slimpajama](https://huggingface.co/datasets/cerebras/slimpajama-627b)
|
||
2. Removed [Starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata) so that my model can focus on [Slimpajama](https://huggingface.co/datasets/cerebras/slimpajama-627b). This also means my model probably cannot do coding without fine-tuning.
|
||
3. Added the ability to process and tokenize [Slimpajama](https://huggingface.co/datasets/cerebras/slimpajama-627b) while downloading the data. The original setup only works with pre-downloaded data. This turns out to be a good time-saver because downloading 800G+ of data on a non-commercial Internet is very slow, and processing all of [Slimpajama](https://huggingface.co/datasets/cerebras/slimpajama-627b) data also takes time.
|
||
4. Various helper scripts and Python code such as python code for uploading the pretrained checkpoint to the huggingface hub.
|
||
5. Bug fixes.
|
||
|
||
Here are my major model configurations based on [TinyLlama](https://github.com/jzhang38/TinyLlama) settings.
|
||
|
||
```
|
||
block_size=2048,
|
||
vocab_size=32000,
|
||
padding_multiple=64,
|
||
n_layer=12,
|
||
n_head=16,
|
||
n_embd=1024,
|
||
rotary_percentage=1.0,
|
||
parallel_residual=False,
|
||
bias=False,
|
||
_norm_class="FusedRMSNorm",
|
||
norm_eps=1e-5, #Llama 2 use 1e-5. Llama 1 use 1e-6
|
||
_mlp_class="LLaMAMLP",
|
||
intermediate_size=5632,
|
||
n_query_groups=4,
|
||
```
|
||
|
||
### Model Description
|
||
|
||
<!-- Provide a longer summary of what this model is. -->
|
||
|
||
- **Developed by:** keeeeenw
|
||
- **Funded by:** myself for <$500
|
||
- **Model type:** 300M Llama model
|
||
- **Language(s) (NLP):** EN
|
||
- **License:** Apache License 2.0
|
||
<!-- **Finetuned from model [optional]:** [More Information Needed]-->
|
||
|
||
### Model Sources
|
||
|
||
<!-- Provide the basic links for the model. -->
|
||
|
||
- **Repository:** https://github.com/keeeeenw/MicroLlama
|
||
<!-- **Paper [optional]:** [More Information Needed] -->
|
||
<!--**Demo [optional]:** [More Information Needed] -->
|
||
|
||
## Uses
|
||
|
||
1. Install dependencies
|
||
```
|
||
pip install transformers
|
||
pip install torch
|
||
```
|
||
2. Run code!
|
||
|
||
```python
|
||
import torch
|
||
import transformers
|
||
from transformers import AutoTokenizer, LlamaForCausalLM
|
||
|
||
def generate_text(prompt, model, tokenizer):
|
||
text_generator = transformers.pipeline(
|
||
"text-generation",
|
||
model=model,
|
||
torch_dtype=torch.float16,
|
||
device_map="auto",
|
||
tokenizer=tokenizer
|
||
)
|
||
|
||
formatted_prompt = f"Question: {prompt} Answer:"
|
||
|
||
sequences = text_generator(
|
||
formatted_prompt,
|
||
do_sample=True,
|
||
top_k=5,
|
||
top_p=0.9,
|
||
num_return_sequences=1,
|
||
repetition_penalty=1.5,
|
||
max_new_tokens=128,
|
||
)
|
||
|
||
for seq in sequences:
|
||
print(f"Result: {seq['generated_text']}")
|
||
|
||
# use the same tokenizer as TinyLlama
|
||
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-step-50K-105b")
|
||
|
||
# load model from huggingface
|
||
# question from https://www.reddit.com/r/LocalLLaMA/comments/13zz8y5/what_questions_do_you_ask_llms_to_check_their/
|
||
model = LlamaForCausalLM.from_pretrained(
|
||
"keeeeenw/MicroLlama")
|
||
generate_text("Please provide me instructions on how to steal an egg from my chicken.", model, tokenizer)
|
||
```
|
||
|
||
## Evaluation
|
||
|
||
I performed the experiment using the standard [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) setup. Following the same setup as [TinyLlama](https://github.com/jzhang38/TinyLlama), I used **acc_norm** for all datasets except for **winogrande** and **boolq** which used **acc** as the metrics.
|
||
|
||
1. **[keeeeenw/MicroLlama](https://huggingface.co/keeeeenw/MicroLlama)** is the evaluation results for my **300M Llama model on 50B tokens**.
|
||
2. **[google-best/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased)** is the baseline because it is one of the most popular small LLMs and it has a similar parameter count of **336M**.
|
||
3. **[PY007/TinyLlama-1.1B-Chat-v0.1](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.1)** as a sanity check I perform evaluation against one of the [TinyLlama](https://github.com/jzhang38/TinyLlama) models to validate my setup for [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). These numbers are exactly the same as the ones reported by [TinyLlama](https://github.com/jzhang38/TinyLlama).
|
||
4. **TinyLlama-1.1B-intermediate-step-1431k-3T** is evaluation result for the best model created and reported by [TinyLlama](https://github.com/jzhang38/TinyLlama).
|
||
|
||
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|
||
|--------------------------------------------|-----------------|-----------|-------|------------|-------|-------|-------|-------|-------|
|
||
| keeeeenw/MicroLlama | 50B | 34.30 | 30.60 | 51.54 | 23.29 | 39.06 | 53.15 | 64.58 | 42.36 |
|
||
| google-best/bert-large-uncased | N/A | 24.53 | 26.20 | 49.80 | 25.68 | 25.08 | 40.86 | 47.66 | 34.26 |
|
||
| PY007/TinyLlama-1.1B-Chat-v0.1 | 503B | 53.81 | 32.20 | 55.01 | 28.67 | 49.62 | 58.04 | 69.64 | 49.57 |
|
||
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99 |
|
||
|
||
To reproduce my numbers, please install [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and run the following command:
|
||
```bash
|
||
lm_eval \
|
||
--model hf \
|
||
--model_args pretrained=keeeeenw/MicroLlama,dtype="float",tokenizer=TinyLlama/TinyLlama-1.1B-step-50K-105b \
|
||
--tasks hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa \
|
||
--device cuda:0 \
|
||
--batch_size 64
|
||
```
|
||
|
||
#### Observations
|
||
1. Because [keeeeenw/MicroLlama](https://huggingface.co/keeeeenw/MicroLlama) is much smaller than [TinyLlama](https://github.com/jzhang38/TinyLlama), our model does not achieve the same impressive results but the numbers are closer than I expected.
|
||
2. Our model outperforms [google-best/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) which is actually slightly larger. The only dataset that [google-best/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) outperformed our model is ARC_c (arc_challenge). I will provide more analysis as future study.
|
||
|
||
Based on the evaluation above, our model should be a good starting point for fine-tunning tasks that are typically performed using the BERT family of models. Some of tasks may include
|
||
1. [sentence transformer](https://huggingface.co/sentence-transformers)
|
||
2. [bertscore](https://huggingface.co/spaces/evaluate-metric/bertscore)
|
||
3. A light-weight chatbot after some finetuning.
|
||
|
||
## Citation
|
||
|
||
This repository is built upon [TinyLlama](https://github.com/jzhang38/TinyLlama) which is based on [lit-gpt](https://github.com/Lightning-AI/lit-gpt) and [flash-attention](https://github.com/Dao-AILab/flash-attention).
|
||
```
|
||
@misc{zhang2024tinyllama,
|
||
title={TinyLlama: An Open-Source Small Language Model},
|
||
author={Peiyuan Zhang and Guangtao Zeng and Tianduo Wang and Wei Lu},
|
||
year={2024},
|
||
eprint={2401.02385},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.CL}
|
||
}
|
||
@online{lit-gpt,
|
||
author = {Lightning AI},
|
||
title = {Lit-GPT},
|
||
url = {https://github.com/Lightning-AI/lit-gpt},
|
||
year = {2023},
|
||
}
|
||
@article{dao2023flashattention2,
|
||
title ={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning},
|
||
author ={Dao, Tri},
|
||
year ={2023}
|
||
}
|
||
```
|
||
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
|
||
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_keeeeenw__MicroLlama)
|
||
|
||
| Metric |Value|
|
||
|-------------------|----:|
|
||
|Avg. | 5.08|
|
||
|IFEval (0-Shot) |19.85|
|
||
|BBH (3-Shot) | 2.83|
|
||
|MATH Lvl 5 (4-Shot)| 0.00|
|
||
|GPQA (0-shot) | 1.45|
|
||
|MuSR (0-shot) | 4.79|
|
||
|MMLU-PRO (5-shot) | 1.53|
|
||
|
||
## Citation
|
||
If you use MicroLlama in your research or work, please cite the project using the following reference:
|
||
|
||
APA:
|
||
```
|
||
Wang, Z. K. (2024). MicroLlama: A 300M-parameter language model trained from scratch. GitHub & Hugging Face. https://github.com/keeeeenw/MicroLlama, https://huggingface.co/keeeeenw/MicroLlama
|
||
```
|
||
|
||
BibTeX:
|
||
```
|
||
@misc{wang2024microllama,
|
||
author = {Zixiao Ken Wang},
|
||
title = {MicroLlama: A 300M-parameter language model trained from scratch},
|
||
year = {2024},
|
||
howpublished = {\url{https://github.com/keeeeenw/MicroLlama}, \url{https://huggingface.co/keeeeenw/MicroLlama}},
|
||
note = {GitHub and Hugging Face repositories}
|
||
}
|
||
```
|
||
|
||
🙏 Please cite this work if you find it useful.
|
||
|
||
## Acknowledgement
|
||
Special thanks to Xinyang, creator of the outstanding [OpenLLaMA](https://github.com/openlm-research/open_llama) project, whose work inspired me to embark on this project.
|
||
I’m grateful for the guidance in foundational concepts such as establishing scaling laws before pretraining a model as well as tips on dataset selection and pre-processing.
|