Devendra Chaplot
@dchaplot
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Building next-gen AI at @MistralAI . Past: Research Scientist at Facebook AI Research. Ph.D. @SCSatCMU , BTech @iitbombay CS.
SF Bay Area
Joined April 2010
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Last Seen Profiles
Pinned Tweet
Super excited to announce Pixtral 12B - the first-ever multimodal model by
@MistralAI
!
- Strong on both text and multimodal benchmarks
- New 400M Vision Encoder
- Supports variable image sizes & multi-image input
- 128 seq len
- Apache 2.0
🔗
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302
Proud to announce:
Mixtral 8x7B -- Mixtral of Experts
- Free to use under Apache 2.0 license
- outperforms Llama 2 70B with 6x faster inference.
- matches or outperforms GPT3.5
- masters English, French, Italian, German and Spanish.
- seq_len = 32K
1/N
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191
1K
Super excited to announce Mistral Large 2
- 123B params - fits on a single H100 node
- Natively Multilingual
- Strong code & reasoning
- SOTA function calling
- Open-weights for non-commercial usage
Blog:
Weights:
1/N
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168
1K
We just released Mixtral-8x22B-v0.1 and Mixtral-8x22B-Instruct-v0.1:
- Free to use under Apache 2.0 license
- Outperforms all open models
- Native function calling
- Masters English, French, Italian, German and Spanish.
- Seq_len = 64K
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1K
We just released mistral-finetune, the official repo and guide on how to fine-tune Mistral open-source models using LoRA:
Also released Mistral-7B-Instruct-v0.3 with support for function calling with Apache 2.0 license:
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138
762
Proud to announce: Mistral Large
- 81.2% MMLU
- outperforms Claude 2, Gemini 1.0 Pro, GPT-3.5
- multilingual with seq_len = 32K
- json mode and function calling
Also excited to launch Le Chat (Try it now at )
🔗
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For students applying for graduate fellowships, I uploaded my research statement from the Facebook Fellowship application last year:
I am happy to answer questions over email. Good luck!
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Excited to announce our first code model: Codestral-22B!
- Fluent in 80+ programming languages
- Outperforms all open code models
- Available on our API and for free on Le Chat
- Integrated with VS Code
- Weights:
- Blog:
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84
515
Presenting SEAL: Self-supervised Embodied Active Learning!
#NeurIPS2021
SEAL is a self-supervised framework to close the action-perception loop. It improves perception & action models by just moving in the physical world w/o any human supervision.
1/N
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We won the CVPR 2020
@ai_habitat
ObjectNav Challenge!
Check out the talk describing our method at:
Slides:
Paper preprint coming soon!
w. D. Gandhi, A. Gupta,
@rsalakhu
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53
409
Proud to present: GOAT: GO to AnyThing
A universal navigation system that can find any object specified in any way - as an image, language, or a category - in completely unseen environments.
Also useful for pick and place and social navigation!
🧵👇
5
44
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Excited to release
@MistralAI
La Plateforme!
Three chat endpoints with competitive pricing:
Mistral-tiny: Mistral 7B Instruct v0.2, upgraded base model with higher context length 8K --> 32K and better finetuning, 6.84 --> 7.61 on MT Bench.
Mistral-small: Mistral 8x7B Instruct
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There's some confusion regarding Mistral-7B-v0.2 Base model:
- This is the base model behind Mistral-7B-Instruct-v0.2 released in Dec, 2023. The base model was not released just because we forgot.
- Primary goal behind v0.2 was to increase the context window from 8K to 32K.
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28
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Posted our ICLR-20 paper on Active Neural SLAM on arXiv. SOTA results for Visual Exploration, CVPR-19 Habitat Challenge Winner, Sim-to-real, code, pretrained models and more!
Webpage:
Code:
PDF:
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Excite to announce: Mistral AI Fine-tuning API
- serverless fine-tuning on La Plateforme
- fast & cost-efficient using LoRA adapters
- easy deployment
Also, open-source library for fine-tuning on your own servers:
Blog:
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192
Robot visual navigation in unseen homes is hard: end-to-end RL works well in sim but gets only 23% real-world success.
Today, in the first real-world empirical study of visual navigation, we show Modular Learning achieves 90% success in unseen homes!
1/N
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New open-source release for the Mistral AI Hackathon
Mistral 7B v0.2 Base:
- 32k context window
- Rope Theta = 1e6
- No sliding window
This is the raw pretrained model behind Mistral-7B-Instruct-v0.2
Also, new fine-tuning repo:
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235
With $640M raise @ 6B+ valuation, Mistral AI has grown ~3x in valuation since December and ~25x in the last 12 months!
Join us to build the next generation of AI models in a small and extremely talented team. We are hiring in Paris, London & SF/Bay Area:
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@AlphaSignalAI
@ClementDelangue
I pretty much only trust two LLM evals right now: Chatbot Arena and r/LocalLlama comments section
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We finally open-sourced the code for our Doom AI agent, Arnold!
PyTorch implementation of "Arnold", our DOOM AI that won the 2017 edition of the ViZDoom competition: , with
@dchaplot
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Excited to announce that our paper on "Navigating to objects in the real world" is published at Science Robotics!
PDF:
w.
@theo_gervet
@soumithchintala
,
@DhruvBatraDB
,
@JitendraMalikCV
@MetaAI
@SCSatCMU
🧵 for more details 👇
Robot visual navigation in unseen homes is hard: end-to-end RL works well in sim but gets only 23% real-world success.
Today, in the first real-world empirical study of visual navigation, we show Modular Learning achieves 90% success in unseen homes!
1/N
3
46
237
4
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161
We released the code, datasets, pre-trained models for our
#NeurIPS2020
paper on Object Goal Navigation and our winning entry to the
#CVPR2020
Habitat ObjectNav challenge!
GitHub Code:
Webpage:
w.
@_dgandhi
, A.Gupta,
@rsalakhu
1
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158
We just posted a new paper on arxiv on global pose estimation using Neural Graph Optimization, making progress towards end-to-end SLAM!
Arxiv:
(with E. Parisotto, J. Zhang, and
@rsalakhu
)
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We just released
@PyTorch
implementation of
#AAAI2018
paper, Gated-Attention Architectures for Task-Oriented Language Grounding:
Environment code and pre-trained models available too (with
@kanthashree
@RamK1729
@dheerajgopal
@rsalakhu
)
0
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Exciting personal update: After 5 years in FAIR and Meta AI, I have joined Mistral AI (
@aimistral
) with
@arthurmensch
,
@GuillaumeLample
and
@tlacroix6
to build the next generation of AI!
8
2
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Posted a new paper on Embodied Multimodal Multitask Learning for semantic goal navigation and embodied question answering. (with
@rl_agent
,
@rsalakhu
,
@deviparikh
,
@DhruvBatraDB
)
PDF:
Demo Videos:
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It's Graduate Fellowship application season again! For students applying for graduate fellowships, I uploaded my research statement from the 2020 Facebook Fellowship application:
I am happy to answer questions over email. Good luck!
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All Mistral-7B v0.1 links in one place!
Blog post with all results and details:
Download Model:
Docker Image:
Our reference implementation:
1/N
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Our CVPR-20 paper "Neural Topological SLAM for Visual Navigation" now on arXiv. Our model uses topological representations to learn semantic priors and can be trained without RL or IL using only static image data.
Webpage:
PDF:
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New paper on Learning Cognitive Models using Neural Networks on arxiv: . We found that learning in neural networks is indicative of human learning based on student performance data in Intelligent Tutoring Systems. With C. MacLellan,
@rsalakhu
, K. Koedinger
2
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Accepted at NeurIPS 2020!👇
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Honored to receive the 2020 Facebook Fellowship award! Thank you, Facebook!😀
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Excited to release Codestral-Mamba 7B:
- Infinite sequence length, tested with 256K tokens
- Linear time inference with Mamba
- Great for local code assistant
- Outperforms Transformers of similar size
- Apache 2.0
- HF:
- Blog:
1
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Excited to present our papers at
#AAAI2018
! If you are around, check out the talks:
Gated-Attention Architectures for Task-Oriented Language Grounding:
Tue, Feb 6 11:45am, Royal
(with
@kanthashree
@RamK1729
@dheerajgopal
@rsalakhu
) ... (1/2)
1
12
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Mistral 7B v0.1 is out!
- Best 7B model
- Outperforms Llama 2 13B on all benchmarks
- Outperforms LLaMA 1 34B on code, math, and reasoning
- Apache 2 license, no restrictions
0
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We also release Mixtral 8x7B Instruct v0.1 trained using supervised fine-tuning and direct preference optimisation (DPO).
It scores 8.3 on MT-Bench making it the best open-source model, with a performance comparable to GPT3.5.
2/N
1
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66
#NeurIPS21
paper on No RL No Simulation (NRNS): Learning to Navigate without Navigating!
NRNS not only beats RL/IL algorithms in simulation but when it comes to real-world…there is no sim2real required!
Webpage:
Code:
(1/2)
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Mixtral-8x22B outperforms all open models across reasoning, knowledge, code and math capabilities while offering the best performance/cost ratio.
Mixtral-8x22B-v0.1 on HF:
Mixtral-8x22B-Instruct-v0.1 on HF:
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Excited to present our
#NeurIPS2020
paper on Object Goal Navigation using Goal-Oriented Semantic Exploration where we describe our winning entry to the
#CVPR2020
Habitat ObjectNav Challenge!
Tue Dec 8, 12-2pm ET
PDF:
Web:
(1/2)
1
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Check out our poster on 'Cross-Task Knowledge Transfer for Visually-Grounded Navigation' at the
#neurips2018
DeepRL Workshop (Friday, 3pm, Room 220).
- with
@rl_agent
@rsalakhu
@deviparikh
@DhruvBatraDB
0
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Code & Reasoning
- Trained with 80+ programming languages
- Mistral Large 2 (123B) comparable to GPT-4o, Opus-3 and Llama-3 405B at coding benchmarks
- As compared to Mistral Large 1, significantly reduced hallucinations, improved reliability
2/N
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Blog:
Download Mixtral-8x7B-v0.1 Base model:
Download Mixtral-8x7B-v0.1 Instruct model:
4/N
2
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54
New
#ICML2021
paper on Differentiable Spatial Planning using Transformers!
TLDR: Transformers enable long-range spatial planning by propagating values between distant points.
Webpage:
Talk:
w.
@pathak2206
, Jitendra Malik
0
9
55
I will be at
#NeurIPS2023
this week along with other
@MistralAI
team members:
@AlbertQJiang
@alexsablay
@Bam4d
@GuillaumeLample
@Fluke_Ellington
@theo_gervet
Please reach out if you want to chat.
4
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We are announcing Habitat-Matterport 3D Semantics Dataset!
216 real-world 3D spaces with dense semantic annotations available to download now:
(1/3) Today we’re releasing the Habitat-Matterport 3D Semantics dataset, the largest public dataset of real-world 3D spaces with dense semantic annotations.
HM3D-Sem is free and available to use with FAIR's Habitat simulator:
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1
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Presenting our
#CVPR2020
paper on "Neural Topological SLAM for Visual Navigation" tomorrow. Join us for the live discussion!
Jun 18, 3pm-5pm PDT
Talk:
Slides:
Paper:
w.
@rsalakhu
, A.Gupta,
@_saurabhg
0
14
48
Semantic Curiosity for Active Visual Learning.
A self-supervised policy taking actions leading to inconsistent object detections. Policy experience helps in improving vision models.
Paper:
Web:
w.
@helenqjiang
, S. Gupta, A. Gupta
2
13
46
Co-organizing the 3rd Embodied AI Workshop at
#CVPR2022
.
We have challenge results, posters, and an amazing set of speakers:
@carolina_parada
,
@RoozbehMottaghi
,
@DhruvBatraDB
, Katerina Fragkiadaki,
@drfeifei
& Jitendra Malik!
Check out for full schedule
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Our recent research is featured in CMU News! The article describes core ideas behind our winning entry to the
#cvpr2020
Habitat ObjectNav Challenge in very simple terms.
For more details:
w.
@_dgandhi
, A.Gupta,
@rsalakhu
If you're looking for a couch, why are you searching the kitchen?
@CarnegieMellon
researchers developing common sense method that helps robots find their way around.
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Mixtral 8x7B is multilingual: vastly outperforms LLaMA 2 70B on French, Italian, German and Spanish with 6x faster inference
3/N
2
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36
Slides from my
#AAAI2018
talks on Language Grounding and WSD:
(with
@kanthashree
@RamK1729
@dheerajgopal
@rsalakhu
)
1
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Recently, we formed a working group to study empirical methodology in navigation research and coordinate future research in this area. We just posted a whitepaper on Evaluation of Embodied Navigation Agents. Thanks to Vladlen for leading the effort!
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Instruction Following
Mistral Large 2 is particularly better at following precise instructions and handling long multi-turn conversations.
On Wild Bench, Arena Hard & MT Bench:
- Outperforms Llama 3.1 405B & Opus-3
- Comparable to Sonnet-3.5 and GPT-4o
3/N
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Multilinguality
- Mistral Large 2 is trained on a large proportion of multilingual data.
- Excels in English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi.
- Outperforms Llama-3.1 70B, Comparable to Llama-3.1 405B
4/N
1
0
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Tool Use & Function Calling
Mistral Large 2 is equipped with enhanced function calling and retrieval skills and has undergone training to proficiently execute both parallel and sequential function call. It achieves state-of-the-art function calling performance.
5/N
3
0
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Accepted at ECCV-20 as spotlight!👇
Semantic Curiosity for Active Visual Learning.
A self-supervised policy taking actions leading to inconsistent object detections. Policy experience helps in improving vision models.
Paper:
Web:
w.
@helenqjiang
, S. Gupta, A. Gupta
2
13
46
0
1
24
I am at ICLR-2024 in Vienna this week along with many others from
@MistralAI
. Please DM if you want to catch up.
We are also hosting an event on Thursday evening with
@a16z
. Sign-up at:
For those at ICLR, we are hosting a rooftop happy hour in central Vienna on Thursday evening together with our friends
@MistralAI
(cc
@GuillaumeLample
@sophiamyang
),
@robrombach
and more in support of OSS AI!!
RSVP here - confirmations required:
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Mistral Large 2 - Access and Customization
- Available today on La Plateforme and Le Chat
- Available on Google Vertex AI, Azure AI Studio, Amazon Bedrock & IBM Watsonx
- Also available for fine-tuning on La Plateforme along with Mistral Nemo 12B and Codestral 22B
6/N
0
1
23
For more details
PDF:
Webpage:
Presentation:
Led by
@theo_gervet
, w.
@soumithchintala
,
@DhruvBatraDB
,
@JitendraMalikCV
(14/N)
2
4
23
Nice article on the progress of AI for games in the past year. Features
@DeepMindAI
's work on Go and Starcraft,
@OpenAI
's work on Dota 2 and two AI systems from
@SCSatCMU
:
@polynoamial
's poker AI Libratus, and our Doom AI Arnold!
@GuillaumeLample
0
11
19
Imitation Learning.
1
4
20
If you are attending
#NeurIPS2021
, visit our poster on Wednesday, Dec 8 at 4.30pm PST!
Presenting SEAL: Self-supervised Embodied Active Learning!
#NeurIPS2021
SEAL is a self-supervised framework to close the action-perception loop. It improves perception & action models by just moving in the physical world w/o any human supervision.
1/N
6
119
516
2
3
20
1
0
18
GOAT is result of several months of hard-work from a large team.
M.Chang,
@theo_gervet
,
@mkulkhanna
,
@yvsriram
,
@shahdhruv_
,
@SoYeonTiffMin
, K.Shah,
@chris_j_paxton
,
@_saurabhg
,
@DhruvBatraDB
,
@RoozbehMottaghi
,
@JitendraMalikCV
Paper:
1
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Also, check out our poster on LSTM Iteration Networks: An Exploration of Differentiable Path Finding in
#ICLR2018
Workshop at 11am on Monday.
(with
@binarymatroid
, Emilio and
@rsalakhu
)
PDF:
0
5
15
SEAL consists of two phases, Action, where we learn an active exploration policy, and Perception, where we train the Perception Model on data gathered using the exploration policy and labels obtained using spatio-temporal label propagation.
2/N
1
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Task: The robot starts in a completely unseen environment and is asked to find an object, let's say a toilet. (2/N)
1
2
12
We conduct the first large-scale real-world empirical evaluation for semantic visual navigation:
- 3 methods
- 6 unseen homes
- 6 goal object categories
All methods are trained in simulation and directly transferred to the real world without any fine-tuning. (5/N)
1
4
12
Nice compilation of recent advances in embodiedAI by
@DhruvBatraDB
& Kristen Grauman. It’s exciting to see the incredible progress made in this space in the past two years, accelerated by the awesome
@ai_habitat
simulator.
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