SN-13B-8k-Instruct is a 13 billion parameter model. It was pretrained as well as instruction tuned on
SambaNova DataScale systems. This model is meant to be used for tasks requiring long sequence understanding.
This model is intended for commercial and research use.
Out-of-Scope Use
SN-13B-8k-Instruct should NOT be used for:
Mission-critical applications
Applications that involve the safety of others
Making highly important decisions
Important automated pipelines
This model is still in early development and can be prone to mistakes and hallucinations, there is still room for improvement. This model is intended to provide the community with a multilingual chat LLM baseline.
Recommendations
Users should be made aware of the risks, biases, limitations, and restrictions of the model, which are listed down at the bottom of the page.
Running the model
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SN-13B-8k-Instruct")4model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SN-13B-8k-Instruct")56prompt ='Define Machine Learning.'7inputs = tokenizer(prompt, return_tensors='pt')89# SN-13B-8k-Instruct occasionally repeats itself when do_sample=False.10# Set do_sample=True when using the model to avoid this.11outputs = model.generate(**inputs, use_cache=True, max_new_tokens=50, do_sample=False)1213print(tokenizer.batch_decode(outputs))
Training Details
Click to expand
Training Procedure
We trained SN-13B-8k-Instruct with SambaNova DataScale systems with
SambaNova's in-house Reconfigurable Dataflow Unit (RDU). We started from random weights, and pretrained for 300 Billion
tokens on sequences of size 2048. We then pretrained for another 250 Billion tokens on sequences of size 8192.
During this phase of training, we curated a dataset that had a large proportion of long sequence articles, with
30% of our articles consisting of greater than 6000 words.
We applied instruction tuning on a variety of tasks derived from datasets such as FLANv2, P3, Natural Instructions, etc.
Hyperparameters
Pretraining on 8k SS
Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
Optimizer: AdamW
Steps: 60000
Global Batch size: 1024
Learning Rate: 1e-5
Learning Rate Scheduler: Fixed
Warmup Steps: 0
Weight decay: 0.1
Instruction-tuned Training
Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
Optimizer: AdamW
Steps: 35000
Global Batch size: 64
Learning Rate: 1e-5
Learning Rate Scheduler: Fixed
Warmup Steps: 0
Weight decay: 0.1
Bias, Risks, and Limitations
Like all LLMs, SN-13B-8k-Instruct has certain limitations:
Hallucination: SN-13B-8k-Instruct may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
Repetition: SN-13B-8k-Instruct may produce repetitive phrases or sentences, leading to less engaging and informative responses.
Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
Toxicity: SN-13B-8k-Instruct may inadvertently generate responses containing inappropriate or harmful content.
Acknowledgment
We appreciate Scrolls and ZeroScrolls for their contributions in creating effective benchmarks to test the long sequence understanding of Large Language Models.
We appreciate lm-eval-harness and HELM for their essential benchmarking contributions,
which were both very helpful in evaluating SN-13B-8k-Instruct's performance. We appreciate the inspiration from the wave of various recent open-source long sequence models,
including XGen, MPT, and
Llama-2 and so on. We look forward to witnessing the continued growth and success of open-source long sequence models.
We highly appreciate the hard work and dedication of these researchers and organizations towards the advancement of the open-source community. Their contributions were invaluable in the development of SN-13B-8k-Instruct, and we hope that our model can contribute to further advancements in the field.
Cite SN-13B-8k-Instruct
@software{sn-13b-8k-instruct,
title = {SN-13B-8k-Instruct: training long sequence size models with SambaNova},
author = {SambaNova Systems},
url = {https://huggingface.co/sambanovasystems/SN-13B-8k-Instruct}
month = {8},
year = {2023},
version = {1.0},
}
