LLaMa-30b-instruct model card
Model Details
- Developed by: Upstage
- Backbone Model: LLaMA
- Variations: It has different model parameter sizes and sequence lengths: 30B/1024, 30B/2048, 65B/1024
- Language(s): English
- Library: HuggingFace Transformers
- License: This model is under a Non-commercial Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out this form, but have either lost your copy of the weights or encountered issues converting them to the Transformers format
- 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
Dataset Details
Used Datasets
Prompt Template
### System:
{System}
### User:
{User}
### Assistant:
{Assistant}
Usage
- 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-30b-instruct")
model = AutoModelForCausalLM.from_pretrained(
"upstage/llama-30b-instruct",
device_map="auto",
torch_dtype=torch.float16,
load_in_8bit=True,
rope_scaling={"type": "dynamic", "factor": 2}
)
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
Evaluation Results
Overview
Main Results
| Model |
H4(Avg) |
ARC |
HellaSwag |
MMLU |
TruthfulQA |
|
MT_Bench |
| 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
Ethical Issues
Ethical Considerations
- There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process
Contact Us
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