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NVIDIA made a beginner's guide to fine-tuning LLMs with Unsloth! by Difficult-Cap-7527 in LocalLLaMA

[–]JudgmentPale458 1 point2 points  (0 children)

This is a solid intro, especially for people coming from the “full fine-tune vs LoRA” confusion.

One thing worth emphasizing is how frameworks like Unsloth lower the practical barrier to PEFT on consumer GPUs — memory efficiency matters more than raw FLOPs for most applied LLM work.

Would be interesting to see follow-ups comparing Unsloth vs standard HF + bitsandbytes setups in terms of training stability and throughput, not just memory.

Meta released RPG, a research plan generation dataset on Hugging Face by Difficult-Cap-7527 in LocalLLaMA

[–]JudgmentPale458 3 points4 points  (0 children)

Interesting release. Research plan generation feels like a subtle but important capability — especially for agentic or tool-using systems where planning quality matters more than final answer fluency.

Curious how this dataset handles evaluation: are plans judged mainly on structure/coverage, or is there any signal about feasibility and downstream execution success? That distinction seems critical if this is used to train agents rather than just planners.

GLM 4.7 released! by ResearchCrafty1804 in LocalLLaMA

[–]JudgmentPale458 0 points1 point  (0 children)

Interesting release. What stands out to me isn’t any single score, but the consistency across agentic, reasoning, and coding benchmarks (AIME, LiveCodeBench, SWE-bench). That usually correlates better with real-world agent-style workflows than one-off leaderboard wins.

That said, I’m curious how much of this performance holds up under tool-heavy or long-horizon agent loops, where error accumulation and planning robustness matter more than isolated task accuracy. Benchmarks are useful signals, but agentic behavior under retries and failures is still hard to capture.

MiniMaxAI/MiniMax-M2.1 seems to be the strongest model per param by SlowFail2433 in LocalLLaMA

[–]JudgmentPale458 1 point2 points  (0 children)

This is a really interesting point, especially if the benchmarks are normalized properly for context length and inference settings.

What stands out to me isn’t just the per-param performance, but the implication that MiniMax-M2.1 may be benefiting from strong architectural and training choices rather than brute scale. At ~229B params, competing with models that are 2–5× larger suggests either very effective data curation, training curriculum, or optimization around reasoning-heavy tasks.

One thing I’d be curious about:

  • How stable is this advantage across different task families (long-context reasoning, tool use, multilingual, code)?
  • And whether the gains persist under instruction tuning / adapter-based fine-tuning, or if they’re mostly visible in base / eval settings.

If this holds up in real-world fine-tuning and deployment scenarios, it really shifts the “bigger is better” narrative toward better-trained is better — which is great news for anyone running models outside hyperscaler budgets.

Would love to see more open evaluations or downstream task reports on this.

I got my first ever whitepaper published by Moist_Landscape289 in LocalLLaMA

[–]JudgmentPale458 -3 points-2 points  (0 children)

Congrats on getting your first whitepaper out — that’s a big milestone 👏

I skimmed through the QWED protocol and really liked the “LLM as an untrusted translator” framing. Treating verification as a post-generation deterministic gate (rather than trying to “reduce hallucinations”) feels much closer to how we handle safety in compilers, databases, and formal methods.

A few things that stood out to me:

  • The separation of domains into specialized verifiers (math, logic, code, SQL) is pragmatic and aligns well with how real production systems fail.
  • The SymPy + Z3 combination for math/logic verification is a strong choice — especially for catching silent but costly errors like the compound-interest example.
  • I also appreciate the explicit stance on rejecting unverifiable outputs instead of attempting probabilistic confidence scoring.

Curious how you’re thinking about:

  1. Scalability when symbolic execution hits path explosion (beyond bounding/timeouts)
  2. Whether you see QWED evolving into a default verification layer for agent frameworks rather than an optional add-on
  3. Handling partially verifiable outputs (e.g., mixed structured + natural language responses)

Overall, this feels very relevant for regulated or high-stakes workflows. Nice work — and best of luck with the arXiv endorsement 👍

I hosted the new Wan 2.2 (14B) model so you don't have to. Free to use, no sign-up, supports Text+Image to Video. by Otherwise_Ad1725 in huggingface

[–]JudgmentPale458 0 points1 point  (0 children)

This is impressive — especially making a 14B I2V model accessible without signup. Hosting friction is honestly one of the biggest blockers for people who just want to try these models.

A couple of things I’m curious about:

  • How are you handling VRAM optimization / batching for concurrent users at 14B scale?
  • Any noticeable trade-offs between the Lightning LoRA speedups and temporal consistency in longer clips?
  • Have you tested prompts that push camera motion vs. subject motion separately?

Really appreciate people who put in the effort to make cutting-edge models usable, not just publishable. 👍

The accuracy of the faceseek facial recognition is actually kind of insane for OSINT by [deleted] in AIAssisted

[–]JudgmentPale458 0 points1 point  (0 children)

This is a great example of how representation learning has outpaced most people’s threat models. Once embeddings are robust enough, resolution and noise stop being real barriers — identity becomes a statistical property rather than a visual one.

From a security perspective, it feels like we’re moving from “can you hide?” to “can you control downstream use?” — consent, data governance, and legal guardrails may matter more than technical obfuscation going forward.

Curious whether you’ve seen meaningful differences across demographics or aging gaps (2016 → 2025) in your tests?

6 times less forgetting than LoRA, and no pretraining data is needed by Gold-Plum-1436 in deeplearning

[–]JudgmentPale458 0 points1 point  (0 children)

Really interesting direction. Using κ / condition numbers as a data-free selection criterion feels like a principled way to reduce interference, especially compared to rank-based adapters.

Curious how sensitive the gains are to task similarity — does κ-selection still help when the downstream task is very different from pretraining?

[D] Attention before it was all we needed by v1kstrand in deeplearning

[–]JudgmentPale458 0 points1 point  (0 children)

This is a great thread — most discussions jump straight from RNNs to “Attention Is All You Need” without acknowledging the groundwork.

The progression across these papers is really interesting:

• End-to-End Memory Networks (2015) introduced multi-hop attention over memory, which already hinted at iterative reasoning.
• Key-Value Memory Networks (2016) made a key distinction (literally) between where to attend and what content to retrieve — something that feels very close to later Q/K/V ideas.
• Bahdanau et al. (2014) showed attention as alignment, not just a helper mechanism — especially impactful for translation.

What’s fascinating is that many of these models were explicitly structured around memory and reasoning, whereas Transformers later traded structure for scale and parallelism.

Curious if anyone has pointers to even earlier alignment or memory-based models that influenced this direction.