Hemant Jain

ML Inference @ Waveforms AI

[Rishabh @Rishabh4275]

𝗪𝗲 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗹𝗼𝗰𝗮𝗹, 𝗽𝗿𝗶𝘃𝗮𝘁𝗲, 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱!
𝗖𝗼𝗯𝗼𝗹𝘁 — a free cross-platform AI assistant that runs entirely locally.

Try Cobolt: https://t.co/cNgnIHSZqj

#LocalAI #OpenSource #MCP #Ollama #Privacy

[Cohere @cohere]

Today, we’re launching early access for North!

Our all-in-one secure AI workspace platform combines LLMs, search, and agents into an intuitive interface that effortlessly integrates AI into your daily work to achieve peak productivity.

[Cohere Labs @Cohere_Labs]

We’ve included detailed guides and a pre-built container..

Let accessible fine-tuning begin 🔥

GitHub repo: https://t.co/oSrH7pmQoP

[Cohere Labs @Cohere_Labs]

@Cohere @CohereForAI has open-sourced a fine-tuning repo and container for easy customizing 🎉

This makes finetuning Command and Aya Expanse open-weight models on @huggingface far more accessible. 🔥

[Weights & Biases @wandb]

Command R fine-tuning just got easier with @cohere & W&B integration. Track experiments, visualize metrics, and optimize—all with one extra line of code. Check it out: https://t.co/LdIs3Fyicp

[Cohere @cohere]

Fine-tuned Command R 08-2024 delivers higher efficiency, offering faster time to first token and greater token throughput compared to larger models, making it a performance-efficient choice for many enterprise use cases.

[Cohere @cohere]

We’ve also developed a seamless integration with @wandb. This allows for enhanced monitoring and evaluation of fine-tuning experiments, providing greater visibility into training efficiency and outcomes.

https://t.co/1V5HtIjObY

[Cohere @cohere]

We're excited to announce updated fine-tuning features alongside fine-tuning support for Command R 08-2024! These new features offer increased flexibility, control over data, and visibility into the fine-tuning process.

https://t.co/EMN4sNO4WY

[Cohere @cohere]

Hemant Jain, Engineering Manager in Finetuning at Cohere, will speak at the @awscloud Booth during @nvidiagtc on March 21 at 11:45 am PDT & 12:20 pm PDT. Come by Booth #708 to ‘Learn the art of model fine-tuning’ and ‘Why Embeddings are the best kept secret in GenAI’.

[Hemant Jain @hemanham]

@IndiGo6E Last year Indigo lost my checked in baggage in a flight from DEL to CCU as part of an international itinerary. My baggage was damaged with cuts and a broken wheel on return.

IndiGo didn't respond to my messages for that either on Twitter.

[Hemant Jain @hemanham]

@IndiGo6E is a classic example of the lack of good engineers & no focus on user experience.

20 mins to get an OTP for a payment that never even asked for one and gets eventually rejected.

30 mins to get an email from their customer service for my flight details.

Do better!

[Hemant Jain @hemanham]

When you try to split a PNR online via their goindigo website it errors out consistently.

If you want to serve the population of 1 billion people, scale and reliability cannot be an afterthought.

My list of complaints could go on 😅

[Cohere @cohere]

We are excited to introduce our comprehensive fine-tuning suite. Businesses can now train and deploy a unique chat, reranking, or multi-label classification model in less than 30 minutes. https://t.co/5ALfkksyPi

[Cohere @cohere]

🚀 Discover how BlueDot turned complex API calls into easy natural language queries with our Classify & Rerank solutions. From 50% to 97% accuracy – a game-changer in global health intelligence. Read our story 📖 and see the power of LLMs in action! https://t.co/II4IuYuBQw

[Marc Benioff @Benioff]

Congratulations @aidangomez on being chosen for the TIME AI 100!

[Cohere @cohere]

Interested in learning about the challenges of serving fine-tuned large language model and ways to overcome them?

Join @hemanham this Thursday 1140am ET at the LLMs and the Generative AI Revolution virtual conference, organized by @AiInfra.

Register now,
https://t.co/mxjtUtRaXv

[Cohere @cohere]

At Cohere, we implement the T-Few technique (Liu et al., 2022) for training and serving custom models.

In this blog post, we delve into the concept of T-Few finetuning, explore its benefits, and explain our implementation workflow.

https://t.co/GrWfX03VkV

[Cohere @cohere]

(1/15) This roundup highlights some interesting NLP papers and resources from 2022 highlighted by @forai_ml’s research community! 

This post has been generated with Cohere. 🧵
https://t.co/VSXwmbMDAZ

[Cohere @cohere]

Attending @PyData NYC? Join @CohereAI's @hemanham on Nov 10th, 11:45am EDT, where he'll give a "gentle intro" to real-world applications of large language models. No prior knowledge needed! Read more about the talk:
https://t.co/w0hV77G7cR

[lablab.ai @lablabai]

Hemant Jain on @CohereAI AI Hackathon 3 🤩

🧑‍💻 Hemant works on ML Inference at Cohere. Previously, he worked at NVIDIA developing the Triton Inference Server, an open-source solution for machine learning models deployment into production.

👉🏼 https://t.co/pUovyqUxl4