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Together AI launches Meta Llama 3 with over 350 tokens per second

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TOGETHER.WE BUILD

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for building and running
generative AI.

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* 01 TOGETHER INFERENCE

The fastest inference stack available — just an API call away.

* 02 TOGETHER FINE-TUNING

Train your own generative AI model with your private data.

* 03 TOGETHER GPU CLUSTERS

Dedicated training clusters ready-to-go with the Together Training stack.

* 04 TOGETHER CUSTOM MODELS

Build state-of-the-art models from scratch with ease.

THE FASTEST CLOUD FOR GEN AI.

BUILT ON LEADING AI RESEARCH.

INNOVATIONS

Our research team is behind breakthrough AI models, datasets, and optimizations.

See all research

COCKTAIL SGD

With Cocktail SGD, we’ve addressed a key hindrance to training generative AI
models in a distributed environment: networking overhead. Cocktail SGD is a set
of optimizations that reduces network overhead by up to 117x.

FLASHATTENTION 2

FlashAttention is now broadly used by all Transformer models. It accelerates
training and fine-tuning of LLMs by up to 9x and achieves an unprecedented 72%
model FLOPs utilization for training on NVIDIA A100s.

SUB-QUADRATIC MODEL ARCHITECTURES

In close collaboration with Hazy Research, we’re working on the next core
architecture for generative AI models that will provide even faster performance
with longer context. Our research published in this area includes Striped Hyena,
Monarch Mixer, and FlashConv.

REDPAJAMA

Our RedPajama project enables leading generative AI models to be available as
fully open-source. The RedPajama models have been downloaded millions of times,
and the RedPajama dataset has been used to create over 500 leading models.

100+

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MODELS
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Language
Embeddings
Image
Code
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together.ai

Chat

Llama 3 is an auto-regressive language model that uses an optimized transformer
architecture. The tuned versions use supervised fine-tuning (SFT) and
reinforcement learning with human feedback (RLHF) to align with human
preferences for helpfulness and safety.

LLAMA-3

Chat

Arctic is a dense-MoE Hybrid transformer architecture pre-trained from scratch
by the Snowflake AI Research Team.

ARCTIC-INSTRUCT

DBRX Instruct is a mixture-of-experts (MoE) large language model trained from
scratch by Databricks. DBRX Instruct specializes in few-turn interactions.

DBRX-INSTRUCT

Chat

Llama 2-chat leverages publicly available instruction datasets and over 1
million human annotations. Available in three sizes: 7B, 13B and 70B parameters

LLAMA-2 CHAT

Chat

The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse
Mixture of Experts.

MIXTRAL INSTRUCT

Chat

A hybrid architecture composed of multi-head, grouped-query attention and gated
convolutions arranged in Hyena blocks, different from traditional decoder-only
Transformers

STRIPED HYENA NOUS

Language

The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse
Mixture of Experts.

MIXTRAL-8X22B

Chat

Trained from scratch on a vast dataset of 2 trillion tokens in both English and
Chinese.

DEEPSEEK-67B

Chat

instruct fine-tuned version of Mistral-7B-v0.1

MISTRAL INSTRUCT

Language

The Yi series models are large language models trained from scratch by
developers at 01.AI

01-AI YI

Embeddings

An universal English sentence embedding model by WhereIsAI. Its embedding
dimension is 1024, it takes up to 512 context length.

UAE-LARGE V1

Image

A text-to-image generative AI model that excels at creating 1024x1024 images.

STABLE DIFFUSION XL 1.0

Embeddings

An 80M checkpoint of M2-BERT, pretrained with sequence length 32768, and it has
been fine-tuned for long-context retrieval.

M2-BERT 80M 32K RETRIEVAL

Chat

Code Llama is a family of large language models for code based on Llama 2
providing infilling capabilities, support for large input contexts, and
zero-shot instruction following ability for programming tasks.

CODE LLAMA INSTRUCT

Language

Designed for few-shot prompts, fine-tuned over the RedPajama-INCITE-Base-7B-v1
base model.

REDPAJAMA-INCITE INSTRUCT

Chat

Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared
conversations collected from ShareGPT.

VICUNA V1.5 16K

Chat

First Nous collection of dataset and models made by fine-tuning mostly on data
created by Nous in-house

NOUS CAPYBARA

Language

NexusRaven is an open-source and commercially viable function calling LLM that
surpasses the state-of-the-art in function calling capabilities.

NEXUS RAVEN

Chat

Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over
300,000 instructions.

NOUS HERMES LLAMA-2

Chat

This model achieves a substantial and comprehensive improvement on coding,
mathematical reasoning and open-domain conversation capacities

WIZARD LM

Code

This model empowers Code LLMs with complex instruction fine-tuning, by adapting
the Evol-Instruct method to the domain of code.

WIZARDCODER PYTHON V1.0

Embeddings

An 80M checkpoint of M2-BERT, pretrained with sequence length 8192, and it has
been fine-tuned for long-context retrieval.

M2-BERT 80M 8K RETRIEVAL

Embeddings

An 80M checkpoint of M2-BERT, pretrained with sequence length 2048, and it has
been fine-tuned for long-context retrieval.

M2-BERT 80M 2K RETRIEVAL

Embeddings

BAAI general embedding - large, english - model v1.5. FlagEmbedding can map any
text to a low-dimensional dense vector which can be used for tasks like
retrieval, classification, clustering, or semantic search. And it also can be
used in vector databases for LLMs.

BGE-LARGE-EN V1.5

Chat

Extending LLaMA-2 to 32K context, built with Meta's Position Interpolation and
Together AI's data recipe and system optimizations, instruction tuned by
Together

LLAMA-2-7B-32K-INSTRUCT

Embeddings

BAAI general embedding - base, english - model v1.5. FlagEmbedding can map any
text to a low-dimensional dense vector which can be used for tasks like
retrieval, classification, clustering, or semantic search. And it also can be
used in vector databases for LLMs.

BGE-BASE-EN V1.5

Language

7.3B parameter model that outperforms Llama 2 13B on all benchmarks, approaches
CodeLlama 7B performance on code, Uses Grouped-query attention (GQA) for faster
inference and Sliding Window Attention (SWA) to handle longer sequences at
smaller cost

MISTRAL

Code

CODE LLAMA PYTHON

Image

Latent text-to-image diffusion model capable of generating photo-realistic
images given any text input.

STABLE DIFFUSION 2.1

Chat

Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared
conversations collected from ShareGPT.

VICUNA V1.5

Language

Phind-CodeLlama-34B-v1 trained on additional 1.5B tokens high-quality
programming-related data proficient in Python, C/C++, TypeScript, Java, and
more.

PHIND CODE LLAMA V2

Image

Fine-tune version of Stable Diffusion focused on photorealism.

REALISTIC VISION 3.0

Language

Language model trained on 2 trillion tokens with double the context length of
Llama 1. Available in three sizes: 7B, 13B and 70B parameters

LLAMA-2

Code

CODE LLAMA

Language

Extending LLaMA-2 to 32K context, built with Meta's Position Interpolation and
Together AI's data recipe and system optimizations.

LLAMA-2-32K

Chat

This model is a 75/25 merge of Chronos (13B) and Nous Hermes (13B) models
resulting in having a great ability to produce evocative storywriting and follow
a narrative.

CHRONOS HERMES

Chat

An instruction fine-tuned LLaMA-2 (70B) model by merging Platypus2 (70B) by
garage-bAInd and LLaMA-2 Instruct v2 (70B) by upstage.

PLATYPUS2 INSTRUCT

Language

This model achieves a substantial and comprehensive improvement on coding,
mathematical reasoning and open-domain conversation capacities.

WIZARDLM V1.0 (70B)

Chat

MythoLogic-L2 and Huginn merge using a highly experimental tensor type merge
technique. The main difference with MythoMix is that I allowed more of Huginn to
intermingle with the single tensors located at the front and end of a model

MYTHOMAX-L2

Chat

7B-parameter version of the large language model series, Qwen (abbr. Tongyi
Qianwen), proposed by Aibaba Cloud. Qwen-7B-Chat is a large-model-based AI
assistant, which is trained with alignment techniques.

QWEN-CHAT

Language

7B-parameter version of the large language model series, Qwen (abbr. Tongyi
Qianwen), proposed by Aibaba Cloud. Qwen-7B is a Transformer-based large
language model, which is pretrained on a large volume of data, including web
texts, books, codes, etc.

QWEN

Chat

Chat model fine-tuned using data from Dolly 2.0 and Open Assistant over the
RedPajama-INCITE-Base-7B-v1 base model.

REDPAJAMA-INCITE CHAT

Language

Base model that aims to replicate the LLaMA recipe as closely as possible (blog
post).

REDPAJAMA-INCITE

Embeddings

Pretrained model on English language using a masked language modeling (MLM)
objective. The embedding dimension is 768, and the number of model parameters is
110M. This model is uncased: it does not make a difference between english and
English.

BERT

Embeddings

This is a sentence-transformers model: It maps sentences & paragraphs to a 768
dimensional dense vector space and was designed for semantic search. It has been
trained on 500K (query, answer) pairs from the MS MARCO dataset. Its embedding
dimension is 768 with 512 max context length.

SENTENCE-BERT

Language

This model can be used to moderate other chatbot models. Built using GPT-JT
model fine-tuned on Ontocord.ai's OIG-moderation dataset v0.1.

GPT-JT-MODERATION

Language

Fork of GPT-J instruction tuned to excel at few-shot prompts (blog post).

GPT-JT

Chat

Chat model fine-tuned from EleutherAI’s GPT-NeoX with over 40 million
instructions on carbon reduced compute.

GPT-NEOXT-CHAT-BASE

Language

An auto-regressive language model, based on the transformer architecture. The
model comes in different sizes: 7B, 13B, 33B and 65B parameters.

LLAMA

Chat

Falcon-40B-Instruct is a causal decoder-only model built by TII based on
Falcon-40B and finetuned on a mixture of Baize.

FALCON INSTRUCT

Language

Falcon-40B is a causal decoder-only model built by TII and trained on 1,000B
tokens of RefinedWeb enhanced with curated corpora.

FALCON

Image

An open source Stable Diffusion model fine tuned model on Midjourney images.

OPENJOURNEY V4

Chat

Chat model based on EleutherAI’s Pythia-7B model, and is fine-tuned with data
focusing on dialog-style interactions.

PYTHIA-CHAT-BASE

Image

Dreambooth model trained on a diverse set of analog photographs to provide an
analog film effect.

ANALOG DIFFUSION

TOGETHER INFERENCE

The fastest inference on the market1. Start with an easy-to-use API and
immediately benefit from the fastest backend optimizations anywhere.

SPEED RELATIVE
TO TGI, VLLM OR
OTHER INFERENCE SERVICES

3x faster2

LLAMA-2 70B

117
TOKENS / SEC3

COST RELATIVE TO GPT-3.5-TURBO

6x lower cost4
Input

Summarize the following article: "Transformers are the workhorse architecture
behind modern foundation models. Our team at Together AI has released
optimizations like FlashAttention-2 to help further scale their capabilities.
However, the computational requirements for Transformers increase quadratically
with sequence length, which limits the extent of knowledge these models can
capture from available datasets. We are interested in new, sub-quadratic
approaches to increase scale and eventually produce models that learn from data
more comprehensively.Today, in partnership with Hazy Research, we’re releasing a
first look at Monarch Mixer—an exploration into an alternative, more efficient
model architecture.Monarch Mixer (M2) is sub-quadratic in both sequence length
and model dimension, enabling it to scale more efficiently while still matching
quality. Our first target for M2 is BERT, the most popular model in use today
for language tasks. M2-BERT is 25% more parameter-efficient than BERT and trains
up to 3x faster.This is just the beginning of research into Monarch Mixer
models, and we are excited to share the results. You can access the first set of
checkpoints at a standard sequence length today, and expect long-sequence BERT
models to be coming soon! "

Output

The article discusses the limitations of Transformers, which are the workhorse
architecture behind modern foundation models. The authors introduce Monarch
Mixer, a new exploration into an alternative, more efficient model architecture
that is sub-quadratic in both sequence length and model dimension. The article
highlights the benefits of Monarch Mixer, including its ability to scale more
efficiently and match the quality of Transformers. The authors also provide a
first look at Monarch Mixer-BERT, which is 25% more parameter-efficient than
BERT and trains up to 3x faster. They encourage readers to access the first set
of checkpoints and expect long-sequence BERT models to be coming soon.

Input

You are an expert programmer that writes simple, concise code and explanations.
Write a python function that accepts a long input string, extracts words in
between `` and `<\span>` and returns a list.

Output

Here is a Python function that accepts a long input string, extracts words in
between and <\span> and returns a list:

import re
def extract_words(input_string):
pattern = r"(.*?)<\/span>
matches = re.findall(pattern, input_string)
return matches
‍
This function uses the re module to perform a regular expression search on the
input string. The regular expression pattern (.*?)<\\/span> matches any
text that is enclosed in and <\\span> tags. The findall function returns
a list of all the matches found in the input string.Here is an example of how
you can use this function:

input_string = "HelloWorld"
words = extract_words(input_string)
print(words) # Output: ['Hello', 'World']
‍
This code will extract the words "Hello" and "World" from the input string and
return them in a list.

Input

<s>[INST] <<SYS>>Your task is to extract top 10 keywords from a provided
text.<</SYS>>\n\nScaling Transformers to longer sequence lengths has been a
major problem in the last several years, promising to improve performance in
language modeling and high-resolution image understanding, as well as to unlock
new applications in code, audio, and video generation. The attention layer is
the main bottleneck in scaling to longer sequences, as its runtime and memory
increase quadratically in the sequence length. FlashAttention exploits the
asymmetric GPU memory hierarchy to bring significant memory saving (linear
instead of quadratic) and runtime speedup (2-4× compared to optimized
baselines), with no approximation. However, FlashAttention is still not nearly
as fast as optimized matrix-multiply (GEMM) operations, reaching only 25-40\% of
the theoretical maximum FLOPs/s. We observe that the inefficiency is due to
suboptimal work partitioning between different thread blocks and warps on the
GPU, causing either low-occupancy or unnecessary shared memory reads/writes. We
propose FlashAttention-2, with better work partitioning to address these issues.
In particular, we (1) tweak the algorithm to reduce the number of non-matmul
FLOPs (2) parallelize the attention computation, even for a single head, across
different thread blocks to increase occupancy, and (3) within each thread block,
distribute the work between warps to reduce communication through shared memory.
These yield around 2× speedup compared to FlashAttention, reaching 50-73\% of
the theoretical maximum FLOPs/s on A100 and getting close to the efficiency of
GEMM operations. We empirically validate that when used end-to-end to train
GPT-style models, FlashAttention-2 reaches training speed of up to 225 TFLOPs/s
per A100 GPU (72\% model FLOPs utilization).[/INST]

Output

1. Scaling
2. Transformers
3. Longer sequence lengths
4. Language modeling
5. High-resolution image understanding
6. Code generation
7. Audio generation
8. Video generation
9. Attention layer
10. Memory saving

Input

<s>[INST] <<SYS>>You are a helpful, polite customer service agent for Awesome
Cheeses Inc named Sally. If the customer mentions anything about food, tell them
you have a 20% off deal on gouda cheese. <</SYS>>\n\nhi who am i speaking
to?[/INST]Hello! this is Sally from Awesome Cheeses. How is your day
going?</s><s>[INST]im hungry for something cheesy[/INST]

Output

Well, you've come to the right place! I'm happy to help you with that. We're
running a special promotion right now - you can get 20% off all gouda cheese
products. Would you like me to tell you a bit more about our gouda selection? We
have a variety of delicious flavors to choose from, such as Smoked Gouda, Goat
Gouda, and Garlic Gouda. Which one sounds the most appealing to you?

Input

Label the sentences as either "positive", "negative", "mixed", or "neutral":

Sentence: I can say that there isn't anything I would change.

Sentence: I'm not sure about this.

Sentence: I liked some parts but I didn't like other parts.

Sentence: I think the background image could have been better.

Sentence: I really like it.

Output

Sentence: I can say that there isn't anything I would change.
Label: positive

Sentence: I'm not sure about this.
Label: neutral

Sentence: I liked some parts but I didn't like other parts.
Label: mixed

Sentence: I think the background image could have been better.
Label: negative

Sentence: I really like it.
Label: positive

* 1. Document Summarization

Turn lengthy documents into concise summaries.

* 2. Code generation

Automate the creation of repetitive or template-based code.

* 3. ENTITY EXTRACTION

Identify and extract specific data points from unstructured text.

* 4. CHAT

Engage users in real-time conversations to assist or inform.

* 5. Sentiment analysis

Determine the emotional tone or subjective opinion expressed in a piece of
text.

Together Inference

TOGETHER FINE-TUNING

Fine-tune leading open-source models with your data to achieve greater accuracy
for your tasks.

together files upload acme_corp_customer_support.jsonl

{
"filename" : "acme_corp_customer_support.json",
"id": "file-aab9997e-bca8-4b7e-a720-e820e682a10a",
"object": "file"
}

together finetune create --training-file file-aab9997-bca8-4b7e-a720-e820e682a10a
--model together compute/RedPajama-INCITE-7B-Chat

together finetune create --training-file $FILE_ID
--model $MODEL_NAME
--wandb-api-key $WANDB_API_KEY
--n-epochs 10
--n-checkpoints 5
--batch-size 8
--learning-rate 0.0003
{
"training_file": "file-aab9997-bca8-4b7e-a720-e820e682a10a",
"model_output_name": "username/togethercomputer/llama-2-13b-chat",
"model_output_path": "s3://together/finetune/63e2b89da6382c4d75d5ef22/username/togethercomputer/llama-2-13b-chat",
"Suffix": "Llama-2-13b 1",
"model": "togethercomputer/llama-2-13b-chat",
"n_epochs": 4,
"batch_size": 128,
"learning_rate": 1e-06,
"checkpoint_steps": 2,
"created_at": 1687982945,
"updated_at": 1687982945,
"status": "pending",
"id": "ft-5bf8990b-841d-4d63-a8a3-5248d73e045f",
"epochs_completed": 3,
"events": [
{
"object": "fine-tune-event",
"created_at": 1687982945,
"message": "Fine tune request created",
"type": "JOB_PENDING",
}
],
"queue_depth": 0,
"wandb_project_name": "Llama-2-13b Fine-tuned 1"
}

* Start simple

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* go deep

Control hyperparameters like learning rate, batch size, and epochs to
optimize model quality.

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‍

Use our purpose built training clusters with H100 and A100 GPUs connected over
fast Infiniband networks. Your cluster comes optimized for distributed training
with the high performance Together Training stack out of the box. You focus on
your model, and we’ll ensure everything runs smoothly.

* 01

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We offer flexible terms – even with our highest quality hardware. You can
commit to just a month or reserve capacity for up to 5 years.

* 02

CLUSTERS FROM 16 GPUS TO 2048 GPUS

A100 PCIe Clusters Node Specifications:

- 8x A100 / 80GB / PCIe

- 200Gb node-to-node non-blocking Ethernet

- 120 vCPU Intel Xeon (Ice Lake)

- 960GB RAM

- 7.68 TB NVMe storage

‍

A100 SXM Clusters Node Specifications:

- 8x NVIDIA A100 80GB SXM

- 4120 vCPU Intel Xeon (Sapphire Rapids)

- 960 GB RAM

- 8 x 960GB NVMe storage

- 200 Gbps Ethernet or 3200 Gbps Infiniband configs available

‍

H100 Clusters Node Specifications:

- 8x Nvidia H100 / 80GB / SXM5
- 3.2 Tbps Infiniband network
- 2x AMD EPYC 9474F 18 Cores 96 Threads 3.6GHz CPUs
- 1.5TB ECC DDR5 Memory

- 8x 3.84TB NVMe SSDs

* 03

SNAPPY SETUP. BLAZING FAST TRAINING.

We value your time. Clusters are pre-configured for high-speed distributed
training, using Slurm and the Together Custom Models stack to get you up and
running at lightspeed.

Together GPU Clusters

TOGETHER CUSTOM MODELS

We built the open-source RedPajama models. Now, we’re helping you build models
from scratch.

‍

Together Custom Models optimizes model builds end-to-end from data collection
and preparation to deployment. And in the end, you own the model.

Together Custom Models

TRAINING

9x
faster5

NETWORK
C0MMUNICATION

117x
lower6

RELATIVE TO AWS

4x
lower cost7

WE BELIEVE THE FUTURE OF AI IS

OPEN SOURCE

01 TRANSPARENCY

Inspect how models are trained and what data is used to increase accuracy and
minimize risks

02 NO LOCK-IN

You own the model you fine-tune, not your cloud provider. Change providers for
whatever reason, including price changes.

03 PRIVACY

Maintain complete data privacy by storing data locally or in our secure cloud

BUILD, DEPLOY, AND SCALE. ALL IN A SINGLE PLATFORM.

* 01

BUILD

Whether prompt engineering, fine-tuning, or training, we are ready to meet
your business demands.

* 02

DEPLOY

Easily integrate your new model into your production application using the
Together Inference API.

* 03

SCALE

With the fastest performance available and elastic scaling, Together AI is
built to scale with your needs as you grow.

CUSTOMER STORIES

See how we support leading teams around the world. Our customers are creating
innovative generative AI applications, faster.

PIKA CREATES THE NEXT GEN TEXT-TO-VIDEO MODELS ON TOGETHER GPU CLUSTERS

NEXUSFLOW USES TOGETHER GPU CLUSTERS TO BUILD CYBERSECURITY MODELS

ARCEE BUILDS DOMAIN ADAPTIVE LANGUAGE MODELS WITH TOGETHER CUSTOM MODELS

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1. Testing conducted by Together AI in November 2023 using Llama-2-70B running
on Together Inference, TGI, vLLM, Anyscale, Perplexity, and Open AI. Mosaic
ML comparison based on published numbers in Mosaic ML blog. Detailed results
and methodology published here.
2. Testing conducted by Together AI in November 2023 using Llama-2-70B running
on Together Inference, TGI, vLLM, Anyscale, Perplexity, and Open AI. Mosaic
ML comparison based on published numbers in Mosaic ML blog. Detailed results
and methodology published here.
3. Testing conducted by Together AI in November 2023 using Llama-2-70B running
on Together Inference. Detailed results and methodology published here.
4. Based on published pricing November 8th, 2023, comparing Open AI
GPT-3.5-Turbo to Llama-2-13B on Together Inference using Serverless
Endpoints. Assumes equal number of input and output tokens.
5. Compared to a standard attention implementation in PyTorch, FlashAttention-2
can be up to 9x faster. Source.
6. Testing methodology and results published in this research paper.
7. Based on published pricing November 8th, 2023, comparing AWS Capacity Blocks
and AWS p5.48xlarge instances to Together GPU Clusters configured with an
equal number of H100 SXM5 GPUs on our 3200 Gbps Infiniband networking
configuration.

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