Llama-3.1-8B-Instruct

Headlines · 3

1. 2d
   Soup v0.76.0 ships layer streaming to fine-tune 8B models on 4 GB laptop GPUs

   Soup is a command-line tool that fine-tunes LLMs from a single YAML config; its v0.76.0 release now refuses at load time settings that were previously accepted but never actually applied, and includes breaking changes such as no longer holding out validation rows by default for grpo, ppo and online_dpo. Its opt-in layer streaming feature (stream_layers: true, still BETA) keeps the frozen base weights out of VRAM and feeds them to the GPU one decoder layer at a time: the project records Llama-3.1-8B-Instruct + NF4 on a 4 GB RTX 3050 Laptop at 3.32 GB peak and 119.6 tok/s (v0.72.2, before the correctness repair), while a later post-repair run reads 208.6 tok/s in a 2.40 GB peak — the project notes the two are not directly comparable because clock speeds differ. The release also evaluates validation loss each epoch or every N steps, and reads the streaming disk tier on a background thread via direct I/O with optional striping across multiple NVMe drives.

   GitHub Trending · Python · 🔥 6
2. 4d
   DeltaTTG trains multi-turn agents with a Token-to-Go progress meter

   An arXiv paper introduces DeltaTTG, which learns a reusable progress signal through a shared progress meter for reinforcement learning of multi-turn agents; its supervision target, Token-to-Go (TTG), is the number of tokens remaining to be generated until task completion, used as an observable proxy for remaining completion effort. The method uses low-cost supervision from recorded trajectories of diverse policies without task-specific progress rules, and during actor training, changes in the frozen meter's estimates redistribute outcome-derived advantages across steps without altering the reward. On ALFWorld, PaperSearchQA and BFCLv3, DeltaTTG improves the average score over GRPO by 5.7 and 7.2 points with Qwen3-4B and Llama-3.1-8B-Instruct respectively, outperforms all compared RL baselines on average while keeping actor-training GPU-hours close to GRPO, and the authors also report improved sample efficiency and out-of-domain performance; code is said to be available.

   Hugging Face · Papers · 🔥 0
3. Oct 9
   Redwood paper tests distillation for auditing and capability transfer

   Redwood Research released a paper testing two uses of distillation for AI safety. Distillation for Incrimination (DFI) distills an untrusted model into a weaker trusted student so auditing the student can incriminate the teacher; on AuditBench's secret-keeping Llama-3.3-70B-Instruct organisms, each fine-tuned with a hidden quirk and trained to deny it, distilled students confessed the quirks far more often than the originals, such as 84% versus 22% for the 'increasing_pep' quirk. DFI worked much less well when the student did not share the teacher's base model (18% with Llama-3.1-8B students versus 65% with Llama-70B) and when teachers lacked introspective awareness of their quirk, while the companion approach, Distillation for Capabilities, transferred capability gains with almost no subliminal animal preference using more epochs on fewer unique examples and inoculation prompting.

   Redwood Research · 🔥 0

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