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

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

Gemma is an open-source model designed by Google. This is Gemma 1.1 7B (IT), an update over the original instruction-tuned Gemma release. Gemma 1.1 was trained using a novel RLHF method, leading to substantial gains on quality, coding capabilities, factuality, instruction following and multi-turn conversation quality.

gemma-1.1-7b-it

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

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.2 generative text model using a variety of publicly available conversation datasets.

Mistral-7B-Instruct-v0.2

LLaMa 2 is a collections of LLMs trained by Meta. This is the 70B chat optimized version. This endpoint has per token pricing.

Llama-2-70b-chat-hf

The Dolphin 2.6 Mixtral 8x7b model is a finetuned version of the Mixtral-8x7b model, trained on a variety of data including coding data, for 3 days on 4 A100 GPUs. It is uncensored and requires trust_remote_code. The model is very obedient and good at coding, but not DPO tuned. The dataset has been filtered for alignment and bias. The model is compliant with user requests and can be used for various purposes such as generating code or engaging in general chat.

dolphin-2.6-mixtral-8x7b

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

OpenChat is a library of open-source language models that have been fine-tuned with C-RLFT, a strategy inspired by offline reinforcement learning. These models can learn from mixed-quality data without preference labels and have achieved exceptional performance comparable to ChatGPT. The developers of OpenChat are dedicated to creating a high-performance, commercially viable, open-source large language model and are continuously making progress towards this goal.

openchat_3.5

LLaVa is a multimodal model that supports vision and language models combined.

llava-1.5-7b-hf

Latest version of the Airoboros model fine-tunned version of llama-2-70b using the Airoboros dataset. This model is currently running jondurbin/airoboros-l2-70b-2.2.1 

airoboros-70b

SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module.

sdxl

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. 

Llama-2-7b-chat-hf

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

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

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

```bash
curl -X POST \
    -d '{"input": {"prompt": "..."}}'  \
    -H 'Content-Type: application/json'  \
    -H "Authorization: bearer $(deepctl auth token)"  \
    'https://api.deepinfra.com/v1/inference/stability-ai/sdxl'
```

The inpu/output is compatible with the cog interface.


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

```bash
deepctl infer \
    -m 'stability-ai/sdxl'  \
    -i prompt=...  \
    -i negative_prompt=...  \
    -i image=@path/to/file
```


prompt

negative_prompt

image

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

mask

width

height

num_outputs

scheduler

num_inference_steps

guidance_scale

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

prompt_strength

Random seed. Leave blank to randomize the seed

seed

refine

for expert_ensemble_refiner, the fraction of noise to use

high_noise_frac

for base_image_refiner, the number of steps to refine, defaults to num_inference_steps

refine_steps

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

stability-ai/sdxl

HTTP/cURL API

Input fields

`prompt`string

`negative_prompt`string

`image`file

`mask`file

`width`integer

`height`integer

`num_outputs`integer

`scheduler`string

`num_inference_steps`integer

`guidance_scale`number

`prompt_strength`number

`seed`integer

`refine`string

`high_noise_frac`number

`refine_steps`integer

`apply_watermark`boolean

Input Schema

Output Schema

stability-ai/sdxl

HTTP/cURL API

Input fields

promptstring

negative_promptstring

imagefile

maskfile

widthinteger

heightinteger

num_outputsinteger

schedulerstring

num_inference_stepsinteger

guidance_scalenumber

prompt_strengthnumber

seedinteger

refinestring

high_noise_fracnumber

refine_stepsinteger

apply_watermarkboolean