Meta developed and released the Meta Llama 3.1 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes

Meta-Llama-3.1-405B-Instruct

Meta-Llama-3.1-70B-Instruct

Meta-Llama-3.1-8B-Instruct

Reflection Llama-3.1 70B is trained with a new technique called Reflection-Tuning that teaches a LLM to detect mistakes in its reasoning and correct course.  The model was trained on synthetic data.

Reflection-Llama-3.1-70B

12B model trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.

Mistral-Nemo-Instruct-2407

MiniCPM-Llama3-V-2_5

FLUX.1-dev is a state-of-the-art 12 billion parameter rectified flow transformer developed by Black Forest Labs. This model excels in text-to-image generation, providing highly accurate and detailed outputs. It is particularly well-regarded for its ability to follow complex prompts and generate anatomically accurate images, especially with challenging details like hands and faces.

FLUX-1-dev

FLUX.1 [schnell] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. This model offers cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives. Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps. 

FLUX-1-schnell

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

whisper-large-v3

Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling.  This is the third and final installment of the Distil-Whisper English series. It the knowledge distilled version of OpenAI's Whisper large-v3, the latest and most performant Whisper model to date.  Compared to previous Distil-Whisper models, the distillation procedure for distil-large-v3 has been adapted to give superior long-form transcription accuracy with OpenAI's sequential long-form algorithm.

distil-large-v3

Text-to-Speech (TTS) technology converts written text into spoken words using advanced speech synthesis. TTS systems are used in applications like virtual assistants, accessibility tools for visually impaired users, and language learning software, enabling seamless human-computer interaction.

Gemma is a family of lightweight, state-of-the-art open models from Google. Gemma-2-27B delivers the best performance for its size class, and even offers competitive alternatives to models more than twice its size. 

gemma-2-27b-it

Gemma is a family of lightweight, state-of-the-art open models from Google. The 9B Gemma 2 model delivers class-leading performance, outperforming Llama 3 8B and other open models in its size category.

gemma-2-9b-it

Euryale 70B v2.1 is a model focused on creative roleplay from Sao10k

L3-70B-Euryale-v2.1

Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes.

Meta-Llama-3-70B-Instruct

The 72 billion parameter Qwen2 excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.

Qwen2-72B-Instruct

Mistral-7B-Instruct-v0.3 is an instruction-tuned model, next iteration of of Mistral 7B that has larger vocabulary, newer tokenizer and supports function calling.

Mistral-7B-Instruct-v0.3

Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes.

Meta-Llama-3-8B-Instruct

This is the instruction fine-tuned version of Mixtral-8x22B - the latest and largest mixture of experts large language model (LLM) from Mistral AI. This state of the art machine learning model uses a mixture 8 of experts (MoE) 22b models. During inference 2 experts are selected. This architecture allows large models to be fast and cheap at inference.

Mixtral-8x22B-Instruct-v0.1

WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to those leading proprietary models.

WizardLM-2-8x22B

WizardLM-2 7B is the smaller variant of Microsoft AI's latest Wizard model. It is the fastest and achieves comparable performance with existing 10x larger open-source leading models

WizardLM-2-7B

Mixtral is mixture of expert large language model (LLM) from Mistral AI. This is state of the art machine learning model using a mixture 8 of experts (MoE) 7b models. During inference 2 expers are selected. This architecture allows large models to be fast and cheap at inference. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks.

Mixtral-8x7B-Instruct-v0.1

A Mythomax/MLewd_13B-style merge of selected 70B models  A multi-model merge of several  LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.

lzlv_70b_fp16_hf

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

bge-large-en-v1.5

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Model has 24 layers and 1024 out dim. 

DeepInfra supports the OpenAI embeddings API.
The following creates an embedding vector representing the input text

```bash
curl "https://api.deepinfra.com/v1/openai/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "intfloat/e5-base-v2",
    "encoding_format": "float"
  }'
```

which will return something similar to

```json
{
  "object":"list",
  "data":[
    {
      "object": "embedding",
      "index":0,
      "embedding":[
        -0.010480394586920738,
        -0.0026091758627444506
        ...
        0.031979579478502274,
        0.02021978422999382
      ]
    }
  ],
  "model": "intfloat/e5-base-v2",
  "usage": {
    "prompt_tokens":12,
    "total_tokens":12
  }
}
```


You can use the official OpenAI python client to create embedding vectors

```python
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/v1/openai",
)

input = "The food was delicious and the waiter...", # or an array ["hello", "world"]

embeddings = openai.embeddings.create(
  model="intfloat/e5-base-v2",
  input=input,
  encoding_format="float"
)

if isinstance(input, str):
    print(embeddings.data[0].embedding)
else:
    for i in range(len(input)):
        print(embeddings.data[i].embedding)

print(embeddings.usage.prompt_tokens)

# [0.0241, 0.5, 1.0, ..., 0.3123, 0.743564]
# 42
```


You can use the official OpenAI Node.js client to create embedding vectors

```javascript
import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: 'https://api.deepinfra.com/v1/openai',
  apiKey: "$DEEPINFRA_TOKEN",
});

const input = "The quick brown fox jumped over the lazy dog" // or an array ["hello", "world"]

async function main() {
  const embedding = await openai.embeddings.create({
    model: "intfloat/e5-base-v2",
    input: input,
    encoding_format: "float",
  });

  // check if input is a string or array
  if (typeof input === "string") {
    console.log(embedding.data[0].embedding);
  } else {
    console.log(embedding.data.map((data) => data.embedding));
  }

  console.log(embedding.usage.prompt_tokens);
}

main();

// [0.0241, 0.5, 1.0, ..., 0.3123, 0.743564]
// 42
```


You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -F 'inputs=["I like chocolate"]'  \
    'https://api.deepinfra.com/v1/inference/intfloat/e5-base-v2'
```

which will give you back something similar to:

```json
{
  "embeddings": [
    [
      0.0,
      0.5,
      1.0
    ],
    [
      1.0,
      0.5,
      0.0
    ]
  ],
  "input_tokens": 42,
  "embedding_jsons": [
    "[0.0, 0.5, 1.0]",
    "[1.0, 0.5, 0.0]"
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/advanced/deepctl) to run
inferences:

```bash
deepctl infer \
    -m 'intfloat/e5-base-v2'  \
    -i 'inputs=["I like chocolate"]'
```

which will give you back something similar to:

```json
{
  "embeddings": [
    [
      0.0,
      0.5,
      1.0
    ],
    [
      1.0,
      0.5,
      0.0
    ]
  ],
  "input_tokens": 42,
  "embedding_jsons": [
    "[0.0, 0.5, 1.0]",
    "[1.0, 0.5, 0.0]"
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


We recommend using our NodeJS client https://github.com/deepinfra/deepinfra-node.

You can install it with

```bash
npm install deepinfra
```

and then

```javascript
import { Embeddings } from "deepinfra";

const DEEPINFRA_API_KEY = "$DEEPINFRA_TOKEN";
const MODEL = "intfloat/e5-base-v2";

const main = async () => {
  const client = new Embeddings(MODEL, DEEPINFRA_API_KEY);
  const body = {
    inputs: [
      "What is the capital of France?",
      "What is the capital of Germany?",
      "What is the capital of Italy?",
    ],
  };
  const output = await client.generate(body);
  console.log(output.embeddings[0]);
};

main();
```


inputs

whether to normalize the computed embeddings

normalize

image

The webhook to call when inference is done, by default you will get the output in the response of your inference request

webhook

Image

Inputs

Normalize

Webhook

EmbeddingsIn

estimated cost billed for the request in USD

Cost

Runtime Ms

Status

Tokens Generated

Tokens Input

Embedding Jsons

the computed embeddings for each input sequence or a single embedding if an image was passed