A new open source model with 10M context window is out. “We gradually increased context size from 32K →… 4M → 10M. This allowed us to prioritize pretraining with shorter sequences in the beginning, thereby offering higher utilization rates.”
LLMS
-
OS rattrape son retard avec des modèles GPT-3.5
By
–
OS is catching up. 10M is a huge window where even with a gpt-3.5 level model you can do a lot
-
6 Essential ChatGPT Prompting Techniques for Success
By
–
Unlock the secrets of effective ChatGPT prompting with these 6 essential techniques! Follow @ingliguori for more insights into AI and digital innovation. Dive into 'The Digital Edge' for an in-depth look: https://
bit.ly/3u4pILl #AI #ChatGPT #TechTips -
ORPO Fine-tuning Configuration for Llama3-8B on Single GPU
By
–
Here's the best config to orpo finetune llama3-8b on argilla capybara on a single 24GB home GPU! Github: https://
github.com/huggingface/au
totrain-advanced/blob/main/configs/llm_finetuning/llama3-8b-orpo.yml
…
You can run this config using AutoTrain: $ pip install -U autotrain-advanced
$ autotrain –config config.yaml/URL to raw config file -
Personas powered by otherside-llama-3-70b announced
By
–
Announcing `Personas`.
— Matt Shumer (@mattshumer_) 9 mai 2024
Powered by otherside-llama-3-70b! https://t.co/JtiSYLjpDgAnnouncing `Personas`. Powered by otherside-llama-3-70b!
-
API Design Consistency with LLM Assistance and Hallucinations
By
–
For me obvious and predictable really is what I'm looking for in code – consistency is the most important factor in API design in my opinion I use LLMs to help with API design all the time these days, I've even had Copilot hallucinations which have inspired me to add the
-
Cohere Command R Fine-tuning Now Available on AWS SageMaker
By
–
Command R with fine-tuning is accessible through the Cohere platform, @awscloud SageMaker, and soon to come on additional platforms! https://
aws.amazon.com/marketplace/pp
/prodview-2czs5tbao7b7c
… -
Command R Fine-Tuning Reduces Inference Costs by 15x
By
–
Maximize efficiency and speed with Command R fine-tuning. This reduces inference costs by 15x compared to larger models and achieves faster throughput, lower latency and superior performance.
-
Command R Fine-Tuning Achieves Best Results Finance Research
By
–
We looked at how Command R with fine-tuning works across industries like finance and scientific research (R&D) with highly specialized terminology and found the model delivers best-in-class results.