Downloads · 30 days
5
12% of all-time downloads
RichardErkhov/h2oai_-_h2o-danube2-1.8b-base-4bits
h2oai_-_h2o-danube2-1.8b-base-4bits is a text generation model from RichardErkhov. Use it when you need the model to write or continue text. It is set up for transformers.
Downloads · 30 days
5
12% of all-time downloads
All-time downloads
41
Public
Parameters
1.9B
1.3 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors1.3 GB · 100%
How the weights are stored.
U81.7B · 90%
From the Hugging Face model README
Quantization made by Richard Erkhov.
h2o-danube2-1.8b-base - bnb 4bits
language:
h2o-danube2-1.8b-base is a foundation model trained by H2O.ai with 1.8 billion parameters. For details, please refer to our Technical Report. We release three versions of this model:
| Model Name | Description |
|---|---|
| h2oai/h2o-danube2-1.8b-base | Base model |
| h2oai/h2o-danube2-1.8b-sft | SFT tuned |
| h2oai/h2o-danube2-1.8b-chat | SFT + DPO tuned |
We adjust the Llama 2 architecture for a total of around 1.8b parameters. We use the Mistral tokenizer with a vocabulary size of 32,000 and train our model up to a context length of 8,192.
The details of the model architecture are:
| Hyperparameter | Value |
|---|---|
| n_layers | 24 |
| n_heads | 32 |
| n_query_groups | 8 |
| n_embd | 2560 |
| vocab size | 32000 |
| sequence length | 8192 |
This is a pre-trained foundation model. For your task, you will likely want to perform application specific fine-tuning. We also offer a chat fine-tuned version: h2oai/h2o-danube2-1.8b-chat.
To use the model with the transformers library on a machine with GPUs, first make sure you have the transformers library installed.
# pip install transformers>=4.39.3
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("h2oai/h2o-danube2-1.8b-base")
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2o-danube2-1.8b-base",
torch_dtype=torch.bfloat16,
)
model.cuda()
inputs = tokenizer("The Danube is the second longest river in Europe", return_tensors="pt").to(model.device)
res = model.generate(
**inputs,
max_new_tokens=38,
do_sample=False,
)
print(tokenizer.decode(res[0], skip_special_tokens=True))
Among models of similar size h2o-danube2-1.8b-base achieves best results (on average) across benchmarks of Open LLM Leaderboard 🤗
| Model | Size | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8k | Average |
|---|---|---|---|---|---|---|---|---|
| StableLM2-1.6B | 1.6B | 43.34 | 70.45 | 38.95 | 36.78 | 64.56 | 17.44 | 45.25 |
| Gemma-2B | 2.5B | 48.46 | 71.65 | 41.68 | 33.13 | 66.77 | 17.36 | 46.51 |
| Qwen1.5-1.8B | 1.8B | 37.88 | 61.42 | 46.71 | 39.43 | 60.30 | 33.59 | 46.55 |
| Phi-1.5 | 1.3B | 52.90 | 63.79 | 43.89 | 40.89 | 72.22 | 12.43 | 47.69 |
| H2O-Danube2 | 1.8B | 43.52 | 73.06 | 40.05 | 38.09 | 68.43 | 29.34 | 48.75 |
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.