Agentic tasks on a budget are moving from cloud powerhouses to compact, locally run models. The hot ticket: finetuning with LoRA and data strategies that unlock tool use without billions of parameters [1].
ellora shows a practical LoRA path: train a LoRA on base commands trajectories for tool calling on 20–30B bases, using 10k–100k trajectories and SFT with tool-use masking; RL steps can be layered later [1].
On-device workflows are getting real. Inferencer lets you inspect token entropy and adjust probabilities on macOS, signaling deeper local control for agentic tasks [2].
People discuss budget rigs for around $1,000. From refurbished Macs to affordable GPUs, the hardware setup is evolving as cloud APIs remain optional rather than required [3].
Proof of concept: MiniModel-200M-Base was trained from scratch on 10B tokens in 110k steps on a single RTX 5090, with peak VRAM under 30 GB and a batch of 64×2048 tokens [4].
Finally, OrKa-reasoning shows 95.6% cost savings with local models plus cognitive orchestration and iterative debate, all open on HuggingFace [5].
Together, these threads sketch a future where agentic power sits in smaller, on-device stacks built with open tools and local orchestration.
References
[D] Training smaller LLM for Agentic tasks.
Discusses training smaller LLMs for agentic tasks; favors finetuning over pretraining; suggests LoRA, data strategies, RLVR resources and tool training
View sourceShow HN: Inferencer – Run and deeply control local AI models (macOS release)
Show HN: Inferencer lets macOS run and manipulate local AI models, showing token entropy and adjusting probabilities.
View sourceWhat’s the best local LLM rig I can put together for around $1000?
Hardware-focused thread debating GPUs, RAM, CPUs for local LLMs, comparing 3090, MI50, V100, Mac options.
View sourceMiniModel-200M-Base
Describes MiniModel-200M-Base trained from scratch on 10B tokens in one day, highlighting efficiency tricks and cross-model comparisons in architecture choices.
View sourceOrKa-reasoning: 95.6% cost savings with local models + cognitive orchestration and high accuracy/success-rate
Orka-reasoning: 95%+ accuracy with local DeepSeek-R1:32b, low cost ($0.131 vs cloud $2.5–$3), multi-agent architecture, open source, HuggingFace.
View source