Gemini

The open source implementation of Gemini, the model that will "eclipse ChatGPT", it seems to work by directly taking in all modalities all at once into a transformer with special decoders for text or img generation!

Join the Agora discord channel to help with the implementation! and Here is the project board:

The input sequences for Gemini consist of texts, audio, images, and videos. These inputs are transformed into tokens, which are then processed by a transformer. Subsequently, conditional decoding takes place to generate image outputs. Interestingly, the architecture of Gemini bears resemblance to Fuyu's architecture but is expanded to encompass multiple modalities. Instead of utilizing a visual transformer (vit) encoder, Gemini simply feeds image embeddings directly into the transformer. For Gemini, the token inputs will likely be indicated by special modality tokens such as [IMG], , [AUDIO], or

Install

pip3 install gemini-torch

Usage

Gemini Transformer Usage

• Base transformer
• Multi Grouped Query Attn / flash attn
• rope
• alibi
• xpos
• qk norm
• no pos embeds
• kv cache

```python import torch

from gemini_torch.model import Gemini

Initialize model with smaller dimensions

model = Gemini( numtokens=50432, maxseqlen=4096, # Reduced from 8192 dim=1280, # Reduced from 2560 depth=16, # Reduced from 32 dimhead=64, # Reduced from 128 heads=12, # Reduced from 24 useabsposemb=False, attnflash=True, attnkvheads=2, qknorm=True, attnqknorm=True, attnqknormdim_scale=True, )

Text shape: [batch, seq_len, dim]

text = torch.randint(0, 50432, (1, 4096)) # Reduced seq_len from 8192

Apply model to text

y = model( text, )

Output shape: [batch, seq_len, dim]

print(y)

```

Full Multi-Modal Gemini

• Processes images and audio through a series of reshapes
• Ready to train for production grade usage
• Hyper optimized with flash attention, qk norm, and other methods

```python import torch

from gemini_torch.model import Gemini

Initialize model with smaller dimensions

model = Gemini( numtokens=10000, # Reduced from 50432 maxseqlen=1024, # Reduced from 4096 dim=320, # Reduced from 1280 depth=8, # Reduced from 16 dimhead=32, # Reduced from 64 heads=6, # Reduced from 12 useabsposemb=False, attnflash=True, attnkvheads=2, qknorm=True, attnqknorm=True, attnqknormdimscale=True, postfusionnorm=True, postmodaltransformnorm=True, )

Text shape: [batch, seq_len, dim]

text = torch.randint(0, 10000, (1, 1024)) # Reduced seq_len from 4096

Img shape: [batch, channels, height, width]

img = torch.randn(1, 3, 64, 64) # Reduced height and width from 128

Audio shape: [batch, audioseqlen, dim]

audio = torch.randn(1, 32) # Reduced audioseqlen from 64

Apply model to text and img

y, _ = model(text=text, img=img, audio=audio)

Output shape: [batch, seq_len, dim]

print(y) print(y.shape)

After much training

model.eval()

text = tokenize(texts) logits = model(text) text = detokenize(logits)

```

LongGemini

An implementation of Gemini with Ring Attention, no multi-modality processing yet.

```python import torch from gemini_torch import LongGemini

Text tokens

x = torch.randint(0, 10000, (1, 1024))

Create an instance of the LongGemini model

model = LongGemini( dim=512, # Dimension of the input tensor depth=32, # Number of transformer blocks dimhead=128, # Dimension of the query, key, and value vectors longgeminidepth=9, # Number of long gemini transformer blocks heads=24, # Number of attention heads qknorm=True, # Whether to apply layer normalization to query and key vectors ringseqsize=512, # The size of the ring sequence )

Apply the model to the input tensor

out = model(x)

Print the output tensor

print(out)

```

Tokenizer

• Sentencepiece, tokenizer
• We're using the same tokenizer as LLAMA with special tokens denoting the beginning and end of the multi modality tokens.
• Does not fully process img, audio, or videos now we need help on that

```python from gemini_torch.tokenizer import MultimodalSentencePieceTokenizer

Example usage

tokenizername = "hf-internal-testing/llama-tokenizer" tokenizer = MultimodalSentencePieceTokenizer(tokenizername=tokenizer_name)

Encoding and decoding examples

encodedaudio = tokenizer.encode("Audio description", modality="audio") decodedaudio = tokenizer.decode(encoded_audio)

print("Encoded audio:", encodedaudio) print("Decoded audio:", decodedaudio) ```

References

• Combine Reinforcment learning with modular pretrained transformer, multi-modal capabilities, image, audio,
• self improving mechanisms like robocat
• PPO? or MPO
• get good at backtracking and exploring alternative paths
• speculative decoding
• Algorithm of Thoughts
• RLHF
• Gemini Report
• Gemini Landing Page

Todo

• [ ] Check out the project board for more todos

• [x] Implement the img feature embedder and align imgs with text and pass into transformer: Gemini models are trained to accommodate textual input interleaved with a wide variety of audio and visual inputs, such as natural images, charts, screenshots, PDFs, and videos, and they can produce text and image outputs (see Figure 2). The visual encoding of Gemini models is inspired by our own foundational work on Flamingo (Alayrac et al., 2022), CoCa (Yu et al., 2022a), and PaLI (Chen et al., 2022), with the important distinction that the models are multimodal from the beginning and can natively output images using discrete image tokens (Ramesh et al., 2021; Yu et al., 2022b).

• [x] Implement the audio processing using USM by Google:In addition, Gemini can directly ingest audio signals at 16kHz from Universal Speech Model (USM) (Zhang et al., 2023) features. This enables the model to capture nuances that are typically lost when the audio is naively mapped to a text input (for example, see audio understanding demo on the website).

• [ ] Video Processing Technique: " Video understanding is accomplished by encoding the video as a sequence of frames in the large context window. Video frames or images can be interleaved naturally with text or audio as part of the model input"

• [ ] Prompting Technique: We find Gemini Ultra achieves highest accuracy when used in combination with a chain-of-thought prompting approach (Wei et al., 2022) that accounts for model uncertainty. The model produces a chain of thought with k samples, for example 8 or 32. If there is a consensus above a preset threshold (selected based on the validation split), it selects this answer, otherwise it reverts to a greedy sample based on maximum likelihood choice without chain of thought. We refer the reader to appendix for a detailed breakdown of how this approach compares with only chain-of-thought prompting or only greedy sampling.

• [ ] Train a 1.8B + 3.25 Model: Nano-1 and Nano-2 model sizes are only 1.8B and 3.25B parameters respectively. Despite their size, they show exceptionally strong performance on factuality, i.e. retrieval-related tasks, and significant performance on reasoning, STEM, coding, multimodal and