#fine-tuning tag

Unsloth Tutorials Aim to Make it Easier to Compare and Fine-tune LLMs

Qwen3-Coder-480B-A35B delivers SOTA advancements in agentic coding and code tasks, matching or outperforming Claude Sonnet-4, GPT-4.1, and Kimi K2. The 480B model achieves a 61.8% on Aider Polygot and supports a 256K token context, extendable to 1M tokens.

Artificial intelligence

fromTechzine Global

3 days ago

Google expands Gemma family with compact 270M variant

Google's Gemma 3 270M is a compact AI model designed for efficient, task-specific fine-tuning at lower operational costs.

fromHackernoon

1 year ago

On Grok and the Weight of Design | HackerNoon

Targeted fine-tuning can lead to systemic behavioral distortions in large-scale models.

#qdylora

fromHackernoon

55 years ago

Artificial intelligence

The Last Rank We Need? QDyLoRA's Vision for the Future of LLM Tuning | HackerNoon

fromHackernoon

1 month ago

Artificial intelligence

Beyond Static Ranks: The Power of Dynamic Quantization in LLM Fine-Tuning | HackerNoon

fromHackernoon

55 years ago

Artificial intelligence

The Last Rank We Need? QDyLoRA's Vision for the Future of LLM Tuning | HackerNoon

fromHackernoon

1 month ago

Artificial intelligence

Beyond Static Ranks: The Power of Dynamic Quantization in LLM Fine-Tuning | HackerNoon

more#qdylora

Artificial intelligence

fromHackernoon

1 month ago

QDyLoRA in Action: Method, Benchmarks, and Why It Outperforms QLoRA | HackerNoon

Quantized DyLoRA achieves superior performance in model fine-tuning tasks compared to previous techniques.

fromHackernoon

55 years ago

Keep the Channel, Change the Filter: A Smarter Way to Fine-Tune AI Models | HackerNoon

Efficient fine-tuning of large pre-trained models can be achieved by adjusting only filter atoms while preserving overall model capabilities.

fromHackernoon

1 month ago

Tuning the Pixels, Not the Soul: How Filter Atoms Remake ConvNets | HackerNoon

Pre-training models on large datasets enhances their performance through fine-tuning for specific tasks.

fromLogRocket Blog

2 months ago

Fine-tuning vs. RAG: Which AI strategy fits your frontend project? - LogRocket Blog

Fine-tuning provides consistent and fast responses, but requires lengthy retraining for updates, while RAG offers instant updates but involves handling latency and interface challenges.

Artificial intelligence

fromHackernoon

3 months ago

Comparing Chameleon AI to Leading Image-to-Text Models | HackerNoon

Chameleon was evaluated on image captioning and visual question-answering tasks against other leading models, focusing on maintaining the fidelity of pre-training data.

#fine-tuning#fine-tuning

Unsloth Tutorials Aim to Make it Easier to Compare and Fine-tune LLMs

Google expands Gemma family with compact 270M variant

On Grok and the Weight of Design | HackerNoon

The Last Rank We Need? QDyLoRA's Vision for the Future of LLM Tuning | HackerNoon

Beyond Static Ranks: The Power of Dynamic Quantization in LLM Fine-Tuning | HackerNoon

The Last Rank We Need? QDyLoRA's Vision for the Future of LLM Tuning | HackerNoon

Beyond Static Ranks: The Power of Dynamic Quantization in LLM Fine-Tuning | HackerNoon

QDyLoRA in Action: Method, Benchmarks, and Why It Outperforms QLoRA | HackerNoon

Keep the Channel, Change the Filter: A Smarter Way to Fine-Tune AI Models | HackerNoon

Tuning the Pixels, Not the Soul: How Filter Atoms Remake ConvNets | HackerNoon

Fine-tuning vs. RAG: Which AI strategy fits your frontend project? - LogRocket Blog

Comparing Chameleon AI to Leading Image-to-Text Models | HackerNoon

#fine-tuning
#fine-tuning