upstage/SOLAR-0-70b-16bit

Updates

Solar, a new bot created by Upstage, is now available on Poe. As a top-ranked model on the HuggingFace Open LLM leaderboard, and a fine tune of Llama 2, Solar is a great example of the progress enabled by open source. Try now at https://poe.com/Solar-0-70b

SOLAR-0-70b-16bit model card

The model name has been changed from LLaMa-2-70b-instruct-v2 to SOLAR-0-70b-16bit

Model Details

• Developed by: Upstage
• Backbone Model: LLaMA-2
• Language(s): English
• Library: HuggingFace Transformers
• License: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license (CC BY-NC-4.0)
• Where to send comments: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the Hugging Face community's model repository
• Contact: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai)

Dataset Details

Used Datasets

• Orca-style dataset
• Alpaca-style dataset
• No other dataset was used except for the dataset mentioned above
• No benchmark test set or the training set are used

Prompt Template

### System:
{System}

### User:
{User}

### Assistant:
{Assistant}

Usage

• The followings are tested on A100 80GB
• Our model can handle up to 10k+ input tokens, thanks to the rope_scaling option

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2")
model = AutoModelForCausalLM.from_pretrained(
"upstage/Llama-2-70b-instruct-v2",
device_map="auto",
torch_dtype=torch.float16,
load_in_8bit=True,
rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
)

prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
del inputs["token_type_ids"]
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

Hardware and Software

• Hardware: We utilized an A100x8 * 4 for training our model
• Training Factors: We fine-tuned this model using a combination of the DeepSpeed library and the HuggingFace Trainer / HuggingFace Accelerate

Evaluation Results

Overview

• We conducted a performance evaluation following the tasks being evaluated on the Open LLM Leaderboard.

We evaluated our model on four benchmark datasets, which include ARC-Challenge, HellaSwag, MMLU, and TruthfulQA We used the lm-evaluation-harness repository, specifically commit b281b0921b636bc36ad05c0b0b0763bd6dd43463.

• We used MT-bench, a set of challenging multi-turn open-ended questions, to evaluate the models

Main Results

| Model | H4(Avg) | ARC | HellaSwag | MMLU | TruthfulQA | | MT_Bench | |--------------------------------------------------------------------|----------|----------|----------|------|----------|-|-------------| | [Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)(*Ours*, *Open LLM Leaderboard*) | 73 | 71.1 | 87.9 | 70.6 | 62.2 | | 7.44063 | | Llama-2-70b-instruct (Ours, Open LLM Leaderboard) | 72.3 | 70.9 | 87.5 | 69.8 | 61 | | 7.24375 | | llama-65b-instruct (Ours, Open LLM Leaderboard) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 | | | | Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 | | | | llama-30b-instruct-2048 (Ours, Open LLM Leaderboard) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | | | | llama-30b-instruct (Ours, Open LLM Leaderboard) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | | | | llama-65b | 64.2 | 63.5 | 86.1 | 63.9 | 43.4 | | | | falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | | |

Scripts for H4 Score Reproduction

• Prepare evaluation environments:

# clone the repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# check out the specific commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# change to the repository directory
cd lm-evaluation-harness

Contact Us

About Upstage

• Upstage is a company specialized in Large Language Models (LLMs) and AI. We will help you build private LLMs and related applications.

If you have a dataset to build domain specific LLMs or make LLM applications, please contact us at ► click here to contact

• As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally.