Synthetic data + #finetuning = smaller, faster models that outperform costly oversized #LLMs. Check out the replay of our recent workshop with @gretel_ai to learn how to build a text-to-sql #codegen model using open-source #SyntheticData. https://
pbase.ai/45v19WO
@predibase
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Synthetic Data and Fine-tuning for Efficient LLM Models
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Predibase Partners with Upstage to Launch Superior Fine-Tuning LLM
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We partnered with @UpstageAI to offer their #SolarLLM – the best LLM for fine-tuning that beats GPT-4 on task-specific AI! Top performing model in 16/31 tasks Outperformed other fine-tuned models 67% to 90% of the time Cost-effective GPU serving
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Build LoRA-Powered AI Systems with Predibase and LoRAX
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Apple's innovative architecture for GenAI is built on small language models (#SLMs) and many fine-tuned #LoRA adapters. See how you can build your own LoRA-powered AI systems today with Predibase and #LoRAX:
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Free $25 Credits to Fine-Tune Small Language Models
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To help get you started, here's $25 free credits on the house. That's enough to fine-tune a handful of SLMs for your use case: https://
lnkd.in/g7jHUDVX. -
Predibase Enables Affordable LoRA Fine-Tuning with LoRAX Technology
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And, with Predibase, fine-tuning LoRA adapters is affordable (~$8), easy (2 lines of code or 2 clicks) and delivers GPT-4 level performance. We also have the secret sauce: #LoRAX (
http://
loraexchange.ai), our open-source for serving 100s of small adapters on a single base model -
Deploy GPT-4 Performance to Snowflake with Open Source Models
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Bring #GPT4 performance to your @SnowflakeDB #DataCloud without spending 10s of thousands of dollars a month And, you can do this with #opensource models—no need to share data w/ a commercial #LLM provider or defer model ownership Save your spot: https://
pbase.ai/3yZ69qx -
LoRA Fine-Tuning with Predibase Speeds Up Inference 3x
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6/ They are only getting better! LoRA fine-tuning with Predibase makes inference 3x faster compared to the base model.
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LoRA Training 4-8x Faster Than Full Fine-Tuning
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4/ Faster to train! Training with LoRA can easily be 4-8x faster than full fine-tuning.
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LoRAX enables serving hundreds of LoRAs from single GPU
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5/ Unlocks multi-model deployments. Serve 100s of LoRAs from a single #GPU deployment with #LoRAX.
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LoRA Training Parameters: Minimal Memory Footprint at 0.2%
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3/ Extremely low memory footprint. LoRA trainable parameters are only 0.2% of total amount of model parameters.