Finetuning

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Abhash Chakraborty

Oct 5

Fine-tuning a 0.8B model to beat hosted LLMs at one job

#machinelearning #llm #finetuning #opensource

7 min read

Phil IPS

Oct 12

Fine-Tuning LLMs with LoRA & QLoRA: Complete Production Guide

#finetuning #lora #qlora #tutorial

8 min read

Phil IPS

Oct 12

Fine-Tuning vs RAG vs Prompt Engineering: When to Use Which

#finetuning #rag #promptengineering #tutorial

5 min read

Phil IPS

Oct 12

Fine-Tuning LLMs on Consumer Hardware: LoRA Guide 2024

#llm #finetuning #lora #tutorial

4 min read

Blessed Josiah

Sep 22

Fine-Tune, Deploy and Use LLM As AI Agent

#llm #ai #finetuning #agents

3 min read

Jaydeep Shah (JD)

Oct 7

What I Learned Trying to Ship a Fine-Tuned Model to the NPU

#edgeai #android #litertlm #finetuning

10 min read

Seth Wheeler

Aug 23

How a Dedup Pass Deleted My Training Curriculum

#llm #measurement #finetuning #security

6 min read

Rickesh T N

Aug 24

A generic fine-tuning playbook, written after doing it wrong several times

#llm #finetuning #machinelearning #mlops

6 min read

Wuic Framework

Aug 31

Teaching a local coding agent from its own mistakes: DPO on a 30B model

#dpo #qlora #finetuning #llm

7 min read

frontendfacile.it

Aug 14

Apex: perché un modello specializzato può battere i “frontier” su React Native

#reactnative #modellispecializzati #contextwindow #finetuning

4 min read

Sara Bezjak

Sep 9

Fine-tuning a content-moderation model on a laptop is easy. Trusting the result is not.

#ai #python #finetuning #machinelearning

8 min read

Tyler Edwards

Sep 8

Prompt engineering vs fine-tuning vs RAG: when to switch

#llm #promptengineering #finetuning #ai

7 min read

PRANJUL RATHOUR

Sep 6

Instruction tuning vs domain adaptation: two different fine-tuning goals

#finetuning #instructiontuning #domainadaptation #llm

3 min read

PRANJUL RATHOUR

Sep 6

Small language models (1B–3B): what they're good for after fine-tuning

#smallmodels #finetuning #llm #deployment

3 min read

PRANJUL RATHOUR

Sep 6

From adapter to deployment: merging LoRA weights and serving with vLLM or a Space

#finetuning #deployment #vllm #serving

3 min read

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